A novel multi-scale and sparsity auto-encoder for classification
Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redunda...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Huiling [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: International journal of machine learning and cybernetics - Heidelberg : Springer, 2010, 13(2022), 12 vom: 17. Sept., Seite 3909-3925 |
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| Übergeordnetes Werk: |
volume:13 ; year:2022 ; number:12 ; day:17 ; month:09 ; pages:3909-3925 |
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| DOI / URN: |
10.1007/s13042-022-01632-5 |
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| Katalog-ID: |
SPR048414530 |
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| 520 | |a Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods. | ||
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10.1007/s13042-022-01632-5 doi (DE-627)SPR048414530 (SPR)s13042-022-01632-5-e DE-627 ger DE-627 rakwb eng Wang, Huiling verfasserin aut A novel multi-scale and sparsity auto-encoder for classification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods. Feature representation (dpeaa)DE-He213 Auto-encoder (dpeaa)DE-He213 Multi-scale feature (dpeaa)DE-He213 L (dpeaa)DE-He213 -norm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Sun, Jun (orcid)0000-0002-9824-4294 aut Gu, Xiaofeng aut Song, Wei aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2022), 12 vom: 17. Sept., Seite 3909-3925 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2022 number:12 day:17 month:09 pages:3909-3925 https://dx.doi.org/10.1007/s13042-022-01632-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2022 12 17 09 3909-3925 |
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10.1007/s13042-022-01632-5 doi (DE-627)SPR048414530 (SPR)s13042-022-01632-5-e DE-627 ger DE-627 rakwb eng Wang, Huiling verfasserin aut A novel multi-scale and sparsity auto-encoder for classification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods. Feature representation (dpeaa)DE-He213 Auto-encoder (dpeaa)DE-He213 Multi-scale feature (dpeaa)DE-He213 L (dpeaa)DE-He213 -norm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Sun, Jun (orcid)0000-0002-9824-4294 aut Gu, Xiaofeng aut Song, Wei aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2022), 12 vom: 17. Sept., Seite 3909-3925 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2022 number:12 day:17 month:09 pages:3909-3925 https://dx.doi.org/10.1007/s13042-022-01632-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2022 12 17 09 3909-3925 |
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10.1007/s13042-022-01632-5 doi (DE-627)SPR048414530 (SPR)s13042-022-01632-5-e DE-627 ger DE-627 rakwb eng Wang, Huiling verfasserin aut A novel multi-scale and sparsity auto-encoder for classification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods. Feature representation (dpeaa)DE-He213 Auto-encoder (dpeaa)DE-He213 Multi-scale feature (dpeaa)DE-He213 L (dpeaa)DE-He213 -norm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Sun, Jun (orcid)0000-0002-9824-4294 aut Gu, Xiaofeng aut Song, Wei aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2022), 12 vom: 17. Sept., Seite 3909-3925 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2022 number:12 day:17 month:09 pages:3909-3925 https://dx.doi.org/10.1007/s13042-022-01632-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2022 12 17 09 3909-3925 |
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10.1007/s13042-022-01632-5 doi (DE-627)SPR048414530 (SPR)s13042-022-01632-5-e DE-627 ger DE-627 rakwb eng Wang, Huiling verfasserin aut A novel multi-scale and sparsity auto-encoder for classification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods. Feature representation (dpeaa)DE-He213 Auto-encoder (dpeaa)DE-He213 Multi-scale feature (dpeaa)DE-He213 L (dpeaa)DE-He213 -norm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Sun, Jun (orcid)0000-0002-9824-4294 aut Gu, Xiaofeng aut Song, Wei aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2022), 12 vom: 17. Sept., Seite 3909-3925 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2022 number:12 day:17 month:09 pages:3909-3925 https://dx.doi.org/10.1007/s13042-022-01632-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2022 12 17 09 3909-3925 |
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10.1007/s13042-022-01632-5 doi (DE-627)SPR048414530 (SPR)s13042-022-01632-5-e DE-627 ger DE-627 rakwb eng Wang, Huiling verfasserin aut A novel multi-scale and sparsity auto-encoder for classification 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods. Feature representation (dpeaa)DE-He213 Auto-encoder (dpeaa)DE-He213 Multi-scale feature (dpeaa)DE-He213 L (dpeaa)DE-He213 -norm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 Sun, Jun (orcid)0000-0002-9824-4294 aut Gu, Xiaofeng aut Song, Wei aut Enthalten in International journal of machine learning and cybernetics Heidelberg : Springer, 2010 13(2022), 12 vom: 17. Sept., Seite 3909-3925 (DE-627)635135132 (DE-600)2572473-3 1868-808X nnns volume:13 year:2022 number:12 day:17 month:09 pages:3909-3925 https://dx.doi.org/10.1007/s13042-022-01632-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2022 12 17 09 3909-3925 |
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Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. 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Wang, Huiling |
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Wang, Huiling misc Feature representation misc Auto-encoder misc Multi-scale feature misc L misc -norm misc Classification A novel multi-scale and sparsity auto-encoder for classification |
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A novel multi-scale and sparsity auto-encoder for classification Feature representation (dpeaa)DE-He213 Auto-encoder (dpeaa)DE-He213 Multi-scale feature (dpeaa)DE-He213 L (dpeaa)DE-He213 -norm (dpeaa)DE-He213 Classification (dpeaa)DE-He213 |
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misc Feature representation misc Auto-encoder misc Multi-scale feature misc L misc -norm misc Classification |
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misc Feature representation misc Auto-encoder misc Multi-scale feature misc L misc -norm misc Classification |
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misc Feature representation misc Auto-encoder misc Multi-scale feature misc L misc -norm misc Classification |
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A novel multi-scale and sparsity auto-encoder for classification |
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novel multi-scale and sparsity auto-encoder for classification |
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A novel multi-scale and sparsity auto-encoder for classification |
| abstract |
Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract The inspiration for generating the multi-scale feature representation originates from the basic observation that multi-scale is closely related to human visual physiological characteristics. Also, since the increase of hidden layer neurons and the amount of data leads to the rise of redundant information, a large amount of calculation makes a model more complex. This paper proposes a novel learning method, namely, multi-scale feature consistency regularization and $ L_{21} $-norm minimization sparse auto-encoder (LR21-MSAE). The multi-scale feature consistency regularization can achieve the latent representations and the visual details while retaining multi-scale information. This method ensures that LR21-MSAE can get valid information for better classification accuracy. By implementing the $ L_{21} $-norm minimization constraint, the LR21-MSAE can adaptively eliminate the potential noise and redundant neurons by enforcing some rows and columns of the weight matrix to be reduced to zero. It can reduce the complexity of the learning model and promote learning sparsity features to generate a compact network. Moreover, introducing the Wasserstein distance in the sparse auto-encoder to measure the difference between the two distributions allows for a more stable training process and faster convergence. To complete the test of the LR21-MSAE model, we choose to conduct the experiments on some publicly available datasets MNIST, Fashion-MNIST, CIFAR-10, USPS, ISOLET, Pendigits, and Ecoli. We demonstrate the advantages of LR21-MSAE, through the experimental results, compared with state-of-the-art feature extraction methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| title_short |
A novel multi-scale and sparsity auto-encoder for classification |
| url |
https://dx.doi.org/10.1007/s13042-022-01632-5 |
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| author2 |
Sun, Jun Gu, Xiaofeng Song, Wei |
| author2Str |
Sun, Jun Gu, Xiaofeng Song, Wei |
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| doi_str |
10.1007/s13042-022-01632-5 |
| up_date |
2024-07-03T19:03:56.862Z |
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| score |
7.4016886 |