Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/579416
Title: Kernel Combined Sparse Representation for Disease Recognition
Authors: Qingxiang Feng;Yicong Zhou
subject: sparse representation|disease recognition|Collaborative representation
Year: 2016
Publisher: IEEE
Abstract: Motivated by the idea that the correlation structure of the entire training set can disclose the relationship between the test sample and the training samples, we propose the combined sparse representation (CSR) classifier for disease recognition. The CSR classifier minimizes the correlation structure of the entire training set multiplied by its transposition and the sparse coefficient together for classification. Including the kernel concept, we propose the kernel combined sparse representation classifier utilizing the high-dimensional nonlinear information instead of the linear information in the CSR classifier. Furthermore, considering the information of the training samples and the class center, we then propose the center-based kernel combined sparse representation (CKCSR) classifier. CKCSR uses the center-based kernel matrix to increase the center-based information that is helpful for classification. The proposed classifiers have been evaluated by extensive experiments on several well-known databases including the EXACT09 database, Emphysema-CT database, mini-MIAS database, Wisconsin breast cancer database, and HD-PECTF database. The experimental results demonstrate that the proposed classifiers achieve better recognition rates than the sparse representation-based classification, collaborative representation based classification, and several state-of-the-art methods.
URI: http://localhost/handle/Hannan/147307
http://localhost/handle/Hannan/579416
ISSN: 1520-9210
1941-0077
volume: 18
issue: 10
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7548373.pdf572.3 kBAdobe PDFThumbnail
Preview File
Title: Kernel Combined Sparse Representation for Disease Recognition
Authors: Qingxiang Feng;Yicong Zhou
subject: sparse representation|disease recognition|Collaborative representation
Year: 2016
Publisher: IEEE
Abstract: Motivated by the idea that the correlation structure of the entire training set can disclose the relationship between the test sample and the training samples, we propose the combined sparse representation (CSR) classifier for disease recognition. The CSR classifier minimizes the correlation structure of the entire training set multiplied by its transposition and the sparse coefficient together for classification. Including the kernel concept, we propose the kernel combined sparse representation classifier utilizing the high-dimensional nonlinear information instead of the linear information in the CSR classifier. Furthermore, considering the information of the training samples and the class center, we then propose the center-based kernel combined sparse representation (CKCSR) classifier. CKCSR uses the center-based kernel matrix to increase the center-based information that is helpful for classification. The proposed classifiers have been evaluated by extensive experiments on several well-known databases including the EXACT09 database, Emphysema-CT database, mini-MIAS database, Wisconsin breast cancer database, and HD-PECTF database. The experimental results demonstrate that the proposed classifiers achieve better recognition rates than the sparse representation-based classification, collaborative representation based classification, and several state-of-the-art methods.
URI: http://localhost/handle/Hannan/147307
http://localhost/handle/Hannan/579416
ISSN: 1520-9210
1941-0077
volume: 18
issue: 10
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7548373.pdf572.3 kBAdobe PDFThumbnail
Preview File
Title: Kernel Combined Sparse Representation for Disease Recognition
Authors: Qingxiang Feng;Yicong Zhou
subject: sparse representation|disease recognition|Collaborative representation
Year: 2016
Publisher: IEEE
Abstract: Motivated by the idea that the correlation structure of the entire training set can disclose the relationship between the test sample and the training samples, we propose the combined sparse representation (CSR) classifier for disease recognition. The CSR classifier minimizes the correlation structure of the entire training set multiplied by its transposition and the sparse coefficient together for classification. Including the kernel concept, we propose the kernel combined sparse representation classifier utilizing the high-dimensional nonlinear information instead of the linear information in the CSR classifier. Furthermore, considering the information of the training samples and the class center, we then propose the center-based kernel combined sparse representation (CKCSR) classifier. CKCSR uses the center-based kernel matrix to increase the center-based information that is helpful for classification. The proposed classifiers have been evaluated by extensive experiments on several well-known databases including the EXACT09 database, Emphysema-CT database, mini-MIAS database, Wisconsin breast cancer database, and HD-PECTF database. The experimental results demonstrate that the proposed classifiers achieve better recognition rates than the sparse representation-based classification, collaborative representation based classification, and several state-of-the-art methods.
URI: http://localhost/handle/Hannan/147307
http://localhost/handle/Hannan/579416
ISSN: 1520-9210
1941-0077
volume: 18
issue: 10
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7548373.pdf572.3 kBAdobe PDFThumbnail
Preview File