Classification approach based on non-negative least squares
Neurocomputing
A novel ensemble algorithm for tumor classification
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Low-resolution image restoration using the combination method of sparse representation and PDE model
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Eigenface-based sparse representation for face recognition
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Unsupervised images segmentation via incremental dictionary learning based sparse representation
Information Sciences: an International Journal
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A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l_1-norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR-based method for tumor classification using gene expression data. A set of metasamples are extracted from the training samples, and then an input testing sample is represented as the linear combination of these metasamples by l_1-regularized least square method. Classification is achieved by using a discriminating function defined on the representation coefficients. Since l_1-norm minimization leads to a sparse solution, the proposed method is called metasample-based SR classification (MSRC). Extensive experiments on publicly available gene expression data sets show that MSRC is efficient for tumor classification, achieving higher accuracy than many existing representative schemes.