Sparse Nonnegative Matrix Factorization for Classification of Gene Expression Data

This paper considers gene expression data classification by discriminative mixture models in which sparseness of training data features controls the learning rate. Our goal is to improve the sparseness of training features reduced by nonnegative matrix factorization (NMF). We use the generalized L_p-norm NMF for reducing the high dimensional gene expression data. Experimental results on four real gene expression datasets show that, the classification accuracy can be significantly improved by using the generalized method, and especially that it is first to adopt L₂-norm NMF for dimension reduction.