Editing Compression Dictionaries toward Refined Compression-Based Feature-Space

This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects <i<x</i< and <i<y</i< through the compression ratio to compress <i<x</i< with <i<y</i< (’s dictionary). S...
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Autor*in:	Hisashi Koga [verfasserIn] Shota Ouchi [verfasserIn] Yuji Nakajima [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	compression-based pattern recognition data compression feature space compression dictionary

Übergeordnetes Werk:	In: Information - MDPI AG, 2010, 13(2022), 6, p 301
Übergeordnetes Werk:	volume:13 ; year:2022 ; number:6, p 301

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/info13060301

Katalog-ID:	DOAJ027322939

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allfields	10.3390/info13060301 doi (DE-627)DOAJ027322939 (DE-599)DOAJa267bf29b93641b7afc5415e2f0aca28 DE-627 ger DE-627 rakwb eng T58.5-58.64 Hisashi Koga verfasserin aut Editing Compression Dictionaries toward Refined Compression-Based Feature-Space 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects <i<x</i< and <i<y</i< through the compression ratio to compress <i<x</i< with <i<y</i< (’s dictionary). Specifically, we focus on the known framework called PRDC, which represents an object <i<x</i< as a compression-ratio vector (CV) that lines up the compression ratios after <i<x</i< is compressed with multiple different dictionaries. By representing an object <i<x</i< as a CV, PRDC makes it possible to apply vector-based pattern recognition techniques to the compression-based pattern recognition. For PRDC, the dimensions, i.e., the dictionaries determine the quality of the CV space. This paper presents a practical technique to modify the chosen dictionaries in order to improve the performance of pattern recognition substantially: First, in order to make the dictionaries independent from each other, our method leaves any word shared by multiple dictionaries in only one dictionary and assures that any pair of dictionaries have no common words. Next, we transfer words among the dictionaries, so that all the dictionaries may keep roughly the same number of words and acquire the descriptive power evenly. The application to real image classification shows that our method increases classification accuracy by up to 8% compared with the case without our method, which demonstrates that our approach to keep the dictionaries independent is effective. compression-based pattern recognition data compression feature space compression dictionary Information technology Shota Ouchi verfasserin aut Yuji Nakajima verfasserin aut In Information MDPI AG, 2010 13(2022), 6, p 301 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:13 year:2022 number:6, p 301 https://doi.org/10.3390/info13060301 kostenfrei https://doaj.org/article/a267bf29b93641b7afc5415e2f0aca28 kostenfrei https://www.mdpi.com/2078-2489/13/6/301 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 6, p 301
spelling	10.3390/info13060301 doi (DE-627)DOAJ027322939 (DE-599)DOAJa267bf29b93641b7afc5415e2f0aca28 DE-627 ger DE-627 rakwb eng T58.5-58.64 Hisashi Koga verfasserin aut Editing Compression Dictionaries toward Refined Compression-Based Feature-Space 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects <i<x</i< and <i<y</i< through the compression ratio to compress <i<x</i< with <i<y</i< (’s dictionary). Specifically, we focus on the known framework called PRDC, which represents an object <i<x</i< as a compression-ratio vector (CV) that lines up the compression ratios after <i<x</i< is compressed with multiple different dictionaries. By representing an object <i<x</i< as a CV, PRDC makes it possible to apply vector-based pattern recognition techniques to the compression-based pattern recognition. For PRDC, the dimensions, i.e., the dictionaries determine the quality of the CV space. This paper presents a practical technique to modify the chosen dictionaries in order to improve the performance of pattern recognition substantially: First, in order to make the dictionaries independent from each other, our method leaves any word shared by multiple dictionaries in only one dictionary and assures that any pair of dictionaries have no common words. Next, we transfer words among the dictionaries, so that all the dictionaries may keep roughly the same number of words and acquire the descriptive power evenly. The application to real image classification shows that our method increases classification accuracy by up to 8% compared with the case without our method, which demonstrates that our approach to keep the dictionaries independent is effective. compression-based pattern recognition data compression feature space compression dictionary Information technology Shota Ouchi verfasserin aut Yuji Nakajima verfasserin aut In Information MDPI AG, 2010 13(2022), 6, p 301 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:13 year:2022 number:6, p 301 https://doi.org/10.3390/info13060301 kostenfrei https://doaj.org/article/a267bf29b93641b7afc5415e2f0aca28 kostenfrei https://www.mdpi.com/2078-2489/13/6/301 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 6, p 301
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allfieldsSound	10.3390/info13060301 doi (DE-627)DOAJ027322939 (DE-599)DOAJa267bf29b93641b7afc5415e2f0aca28 DE-627 ger DE-627 rakwb eng T58.5-58.64 Hisashi Koga verfasserin aut Editing Compression Dictionaries toward Refined Compression-Based Feature-Space 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects <i<x</i< and <i<y</i< through the compression ratio to compress <i<x</i< with <i<y</i< (’s dictionary). Specifically, we focus on the known framework called PRDC, which represents an object <i<x</i< as a compression-ratio vector (CV) that lines up the compression ratios after <i<x</i< is compressed with multiple different dictionaries. By representing an object <i<x</i< as a CV, PRDC makes it possible to apply vector-based pattern recognition techniques to the compression-based pattern recognition. For PRDC, the dimensions, i.e., the dictionaries determine the quality of the CV space. This paper presents a practical technique to modify the chosen dictionaries in order to improve the performance of pattern recognition substantially: First, in order to make the dictionaries independent from each other, our method leaves any word shared by multiple dictionaries in only one dictionary and assures that any pair of dictionaries have no common words. Next, we transfer words among the dictionaries, so that all the dictionaries may keep roughly the same number of words and acquire the descriptive power evenly. The application to real image classification shows that our method increases classification accuracy by up to 8% compared with the case without our method, which demonstrates that our approach to keep the dictionaries independent is effective. compression-based pattern recognition data compression feature space compression dictionary Information technology Shota Ouchi verfasserin aut Yuji Nakajima verfasserin aut In Information MDPI AG, 2010 13(2022), 6, p 301 (DE-627)654746753 (DE-600)2599790-7 20782489 nnns volume:13 year:2022 number:6, p 301 https://doi.org/10.3390/info13060301 kostenfrei https://doaj.org/article/a267bf29b93641b7afc5415e2f0aca28 kostenfrei https://www.mdpi.com/2078-2489/13/6/301 kostenfrei https://doaj.org/toc/2078-2489 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2022 6, p 301
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authorswithroles_txt_mv	Hisashi Koga @@aut@@ Shota Ouchi @@aut@@ Yuji Nakajima @@aut@@
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callnumber-first	T - Technology
author	Hisashi Koga
spellingShingle	Hisashi Koga misc T58.5-58.64 misc compression-based pattern recognition misc data compression misc feature space misc compression dictionary misc Information technology Editing Compression Dictionaries toward Refined Compression-Based Feature-Space
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topic_title	T58.5-58.64 Editing Compression Dictionaries toward Refined Compression-Based Feature-Space compression-based pattern recognition data compression feature space compression dictionary
topic	misc T58.5-58.64 misc compression-based pattern recognition misc data compression misc feature space misc compression dictionary misc Information technology
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title	Editing Compression Dictionaries toward Refined Compression-Based Feature-Space
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title_full	Editing Compression Dictionaries toward Refined Compression-Based Feature-Space
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title_sort	editing compression dictionaries toward refined compression-based feature-space
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title_auth	Editing Compression Dictionaries toward Refined Compression-Based Feature-Space
abstract	This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects <i<x</i< and <i<y</i< through the compression ratio to compress <i<x</i< with <i<y</i< (’s dictionary). Specifically, we focus on the known framework called PRDC, which represents an object <i<x</i< as a compression-ratio vector (CV) that lines up the compression ratios after <i<x</i< is compressed with multiple different dictionaries. By representing an object <i<x</i< as a CV, PRDC makes it possible to apply vector-based pattern recognition techniques to the compression-based pattern recognition. For PRDC, the dimensions, i.e., the dictionaries determine the quality of the CV space. This paper presents a practical technique to modify the chosen dictionaries in order to improve the performance of pattern recognition substantially: First, in order to make the dictionaries independent from each other, our method leaves any word shared by multiple dictionaries in only one dictionary and assures that any pair of dictionaries have no common words. Next, we transfer words among the dictionaries, so that all the dictionaries may keep roughly the same number of words and acquire the descriptive power evenly. The application to real image classification shows that our method increases classification accuracy by up to 8% compared with the case without our method, which demonstrates that our approach to keep the dictionaries independent is effective.
abstractGer	This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects <i<x</i< and <i<y</i< through the compression ratio to compress <i<x</i< with <i<y</i< (’s dictionary). Specifically, we focus on the known framework called PRDC, which represents an object <i<x</i< as a compression-ratio vector (CV) that lines up the compression ratios after <i<x</i< is compressed with multiple different dictionaries. By representing an object <i<x</i< as a CV, PRDC makes it possible to apply vector-based pattern recognition techniques to the compression-based pattern recognition. For PRDC, the dimensions, i.e., the dictionaries determine the quality of the CV space. This paper presents a practical technique to modify the chosen dictionaries in order to improve the performance of pattern recognition substantially: First, in order to make the dictionaries independent from each other, our method leaves any word shared by multiple dictionaries in only one dictionary and assures that any pair of dictionaries have no common words. Next, we transfer words among the dictionaries, so that all the dictionaries may keep roughly the same number of words and acquire the descriptive power evenly. The application to real image classification shows that our method increases classification accuracy by up to 8% compared with the case without our method, which demonstrates that our approach to keep the dictionaries independent is effective.
abstract_unstemmed	This paper investigates how to construct a feature space for compression-based pattern recognition which judges the similarity between two objects <i<x</i< and <i<y</i< through the compression ratio to compress <i<x</i< with <i<y</i< (’s dictionary). Specifically, we focus on the known framework called PRDC, which represents an object <i<x</i< as a compression-ratio vector (CV) that lines up the compression ratios after <i<x</i< is compressed with multiple different dictionaries. By representing an object <i<x</i< as a CV, PRDC makes it possible to apply vector-based pattern recognition techniques to the compression-based pattern recognition. For PRDC, the dimensions, i.e., the dictionaries determine the quality of the CV space. This paper presents a practical technique to modify the chosen dictionaries in order to improve the performance of pattern recognition substantially: First, in order to make the dictionaries independent from each other, our method leaves any word shared by multiple dictionaries in only one dictionary and assures that any pair of dictionaries have no common words. Next, we transfer words among the dictionaries, so that all the dictionaries may keep roughly the same number of words and acquire the descriptive power evenly. The application to real image classification shows that our method increases classification accuracy by up to 8% compared with the case without our method, which demonstrates that our approach to keep the dictionaries independent is effective.
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title_short	Editing Compression Dictionaries toward Refined Compression-Based Feature-Space
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Editing Compression Dictionaries toward Refined Compression-Based Feature-Space

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