Low Rank Variation Dictionary and Inverse Projection Group Sparse Representation Model for Breast Tumor Classification
This work addresses the problem of small sample size in medical diagnosis for breast tumor classification, offering an incremental improvement over existing sparse representation methods.
The paper tackles breast tumor classification from gene expression data with limited samples by introducing a low-rank variation dictionary and an inverse-projection group sparse representation model, achieving accuracies of 80.81%, 89.10%, and 100% on three public datasets, outperforming state-of-the-art methods.
Sparse representation classification achieves good results by addressing recognition problem with sufficient training samples per subject. However, SRC performs not very well for small sample data. In this paper, an inverse-projection group sparse representation model is presented for breast tumor classification, which is based on constructing low-rank variation dictionary. The proposed low-rank variation dictionary tackles tumor recognition problem from the viewpoint of detecting and using variations in gene expression profiles of normal and patients, rather than directly using these samples. The inverse projection group sparsity representation model is constructed based on taking full using of exist samples and group effect of microarray gene data. Extensive experiments on public breast tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods. The results of Breast-1, Breast-2 and Breast-3 databases are 80.81%, 89.10% and 100% respectively, which are better than the latest literature.