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General Regression and Representation Model for Classification
Recently, the regularized coding-based classification methods (e.g. SRC and CRC) show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this pape...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4274033/ https://www.ncbi.nlm.nih.gov/pubmed/25531882 http://dx.doi.org/10.1371/journal.pone.0115214 |
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author | Qian, Jianjun Yang, Jian Xu, Yong |
author_facet | Qian, Jianjun Yang, Jian Xu, Yong |
author_sort | Qian, Jianjun |
collection | PubMed |
description | Recently, the regularized coding-based classification methods (e.g. SRC and CRC) show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR) for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients) and the specific information (weight matrix of image pixels) to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel) weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR) and robust general regression and representation classifier (R-GRR). The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms. |
format | Online Article Text |
id | pubmed-4274033 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-42740332014-12-31 General Regression and Representation Model for Classification Qian, Jianjun Yang, Jian Xu, Yong PLoS One Research Article Recently, the regularized coding-based classification methods (e.g. SRC and CRC) show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR) for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients) and the specific information (weight matrix of image pixels) to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel) weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR) and robust general regression and representation classifier (R-GRR). The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms. Public Library of Science 2014-12-22 /pmc/articles/PMC4274033/ /pubmed/25531882 http://dx.doi.org/10.1371/journal.pone.0115214 Text en © 2014 Qian et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Qian, Jianjun Yang, Jian Xu, Yong General Regression and Representation Model for Classification |
title | General Regression and Representation Model for Classification |
title_full | General Regression and Representation Model for Classification |
title_fullStr | General Regression and Representation Model for Classification |
title_full_unstemmed | General Regression and Representation Model for Classification |
title_short | General Regression and Representation Model for Classification |
title_sort | general regression and representation model for classification |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4274033/ https://www.ncbi.nlm.nih.gov/pubmed/25531882 http://dx.doi.org/10.1371/journal.pone.0115214 |
work_keys_str_mv | AT qianjianjun generalregressionandrepresentationmodelforclassification AT yangjian generalregressionandrepresentationmodelforclassification AT xuyong generalregressionandrepresentationmodelforclassification |