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Maxdenominator Reweighted Sparse Representation for Tumor Classification

View Article: PubMed Central - PubMed

ABSTRACT

The classification of tumors is crucial for the proper treatment of cancer. Sparse representation-based classifier (SRC) exhibits good classification performance and has been successfully used to classify tumors using gene expression profile data. In this study, we propose a three-step maxdenominator reweighted sparse representation classification (MRSRC) method to classify tumors. First, we extract a set of metagenes from the training samples. These metagenes can capture the structures inherent to the data and are more effective for classification than the original gene expression data. Second, we use a reweighted regularization method to obtain the sparse representation coefficients. Reweighted regularization can enhance sparsity and obtain better sparse representation coefficients. Third, we classify the data by utilizing a maxdenominator residual error function. Maxdenominator strategy can reduce the residual error and improve the accuracy of the final classification. Extensive experiments using publicly available gene expression profile data sets show that the performance of MRSRC is comparable with or better than many existing representative methods.

No MeSH data available.


The value of the sparse representation coefficients of MSRC and MRSRC on four multiclass classification datasets when choosing one sample as test set.
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f7: The value of the sparse representation coefficients of MSRC and MRSRC on four multiclass classification datasets when choosing one sample as test set.

Mentions: To further analyze the results, we compared the value of coefficients of MSRC and MRSRC for the eight data sets. Figures 6 and 7 show the value of the sparse representation coefficients of the training samples. A test samples is represented as the linear relation of metagenes of such training samples. From Figs 6 and 7, we can observe that MRSRC obtains better sparse representation coefficients. MRSRC demonstrates more coefficients, which are equal to zero and are close to the theoretical results.


Maxdenominator Reweighted Sparse Representation for Tumor Classification
The value of the sparse representation coefficients of MSRC and MRSRC on four multiclass classification datasets when choosing one sample as test set.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5385541&req=5

f7: The value of the sparse representation coefficients of MSRC and MRSRC on four multiclass classification datasets when choosing one sample as test set.
Mentions: To further analyze the results, we compared the value of coefficients of MSRC and MRSRC for the eight data sets. Figures 6 and 7 show the value of the sparse representation coefficients of the training samples. A test samples is represented as the linear relation of metagenes of such training samples. From Figs 6 and 7, we can observe that MRSRC obtains better sparse representation coefficients. MRSRC demonstrates more coefficients, which are equal to zero and are close to the theoretical results.

View Article: PubMed Central - PubMed

ABSTRACT

The classification of tumors is crucial for the proper treatment of cancer. Sparse representation-based classifier (SRC) exhibits good classification performance and has been successfully used to classify tumors using gene expression profile data. In this study, we propose a three-step maxdenominator reweighted sparse representation classification (MRSRC) method to classify tumors. First, we extract a set of metagenes from the training samples. These metagenes can capture the structures inherent to the data and are more effective for classification than the original gene expression data. Second, we use a reweighted regularization method to obtain the sparse representation coefficients. Reweighted regularization can enhance sparsity and obtain better sparse representation coefficients. Third, we classify the data by utilizing a maxdenominator residual error function. Maxdenominator strategy can reduce the residual error and improve the accuracy of the final classification. Extensive experiments using publicly available gene expression profile data sets show that the performance of MRSRC is comparable with or better than many existing representative methods.

No MeSH data available.