Projection-optimal local Fisher discriminant analysis for feature extraction
Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the inter...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Zhan [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© The Natural Computing Applications Forum 2014 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 26(2014), 3 vom: 04. Nov., Seite 589-601 |
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| Übergeordnetes Werk: |
volume:26 ; year:2014 ; number:3 ; day:04 ; month:11 ; pages:589-601 |
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| DOI / URN: |
10.1007/s00521-014-1768-9 |
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OLC2025596138 |
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| 520 | |a Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. | ||
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10.1007/s00521-014-1768-9 doi (DE-627)OLC2025596138 (DE-He213)s00521-014-1768-9-p DE-627 ger DE-627 rakwb eng 004 VZ Wang, Zhan verfasserin aut Projection-optimal local Fisher discriminant analysis for feature extraction 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2014 Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. Dimensionality reduction Linear discriminant analysis Local Fisher discriminant analysis Feature extraction Ruan, Qiuqi aut An, Gaoyun aut Enthalten in Neural computing & applications Springer London, 1993 26(2014), 3 vom: 04. Nov., Seite 589-601 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:26 year:2014 number:3 day:04 month:11 pages:589-601 https://doi.org/10.1007/s00521-014-1768-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 26 2014 3 04 11 589-601 |
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10.1007/s00521-014-1768-9 doi (DE-627)OLC2025596138 (DE-He213)s00521-014-1768-9-p DE-627 ger DE-627 rakwb eng 004 VZ Wang, Zhan verfasserin aut Projection-optimal local Fisher discriminant analysis for feature extraction 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2014 Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. Dimensionality reduction Linear discriminant analysis Local Fisher discriminant analysis Feature extraction Ruan, Qiuqi aut An, Gaoyun aut Enthalten in Neural computing & applications Springer London, 1993 26(2014), 3 vom: 04. Nov., Seite 589-601 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:26 year:2014 number:3 day:04 month:11 pages:589-601 https://doi.org/10.1007/s00521-014-1768-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 26 2014 3 04 11 589-601 |
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10.1007/s00521-014-1768-9 doi (DE-627)OLC2025596138 (DE-He213)s00521-014-1768-9-p DE-627 ger DE-627 rakwb eng 004 VZ Wang, Zhan verfasserin aut Projection-optimal local Fisher discriminant analysis for feature extraction 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2014 Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. Dimensionality reduction Linear discriminant analysis Local Fisher discriminant analysis Feature extraction Ruan, Qiuqi aut An, Gaoyun aut Enthalten in Neural computing & applications Springer London, 1993 26(2014), 3 vom: 04. Nov., Seite 589-601 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:26 year:2014 number:3 day:04 month:11 pages:589-601 https://doi.org/10.1007/s00521-014-1768-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 26 2014 3 04 11 589-601 |
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10.1007/s00521-014-1768-9 doi (DE-627)OLC2025596138 (DE-He213)s00521-014-1768-9-p DE-627 ger DE-627 rakwb eng 004 VZ Wang, Zhan verfasserin aut Projection-optimal local Fisher discriminant analysis for feature extraction 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2014 Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. Dimensionality reduction Linear discriminant analysis Local Fisher discriminant analysis Feature extraction Ruan, Qiuqi aut An, Gaoyun aut Enthalten in Neural computing & applications Springer London, 1993 26(2014), 3 vom: 04. Nov., Seite 589-601 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:26 year:2014 number:3 day:04 month:11 pages:589-601 https://doi.org/10.1007/s00521-014-1768-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 26 2014 3 04 11 589-601 |
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10.1007/s00521-014-1768-9 doi (DE-627)OLC2025596138 (DE-He213)s00521-014-1768-9-p DE-627 ger DE-627 rakwb eng 004 VZ Wang, Zhan verfasserin aut Projection-optimal local Fisher discriminant analysis for feature extraction 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Natural Computing Applications Forum 2014 Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. Dimensionality reduction Linear discriminant analysis Local Fisher discriminant analysis Feature extraction Ruan, Qiuqi aut An, Gaoyun aut Enthalten in Neural computing & applications Springer London, 1993 26(2014), 3 vom: 04. Nov., Seite 589-601 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:26 year:2014 number:3 day:04 month:11 pages:589-601 https://doi.org/10.1007/s00521-014-1768-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4046 GBV_ILN_4277 AR 26 2014 3 04 11 589-601 |
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Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. © The Natural Computing Applications Forum 2014 |
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Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. © The Natural Computing Applications Forum 2014 |
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Abstract In this paper, a novel dimensionality reduction algorithm called projection-optimal local Fisher discriminant analysis (PoLFDA) is proposed in order to address the multimodal problem. Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. Experiments on toy data, face, and handwritten digit data sets are conducted to evaluate the performance of PoLFDA; the results and comparisons verify the effectiveness of the proposed method. © The Natural Computing Applications Forum 2014 |
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10.1007/s00521-014-1768-9 |
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Novel weight matrices defined on the projected space can represent the intraclass compactness and the interclass separability. Based on the novel weighted matrices, the local between-class scatter matrix and the local within-class scatter matrix are defined such that the local structure can be preserved. In order to enhance the discriminant ability, we impose an orthogonal constraint on the objective function, which can be regarded as a trace ratio problem. In general, a trace ratio problem does not have a closed-form solution; however, it can be solved using some efficient iterative algorithms. Therefore, we optimize the projection matrix by solving the trace ratio problem iteratively. 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