  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.
