Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification

摘要: Advances in DNA microarray technologies have made gene expression profiles a significant candidate identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train classifier, but they are inconvenient for practical application because labels quite expensive the clinical research community. This paper proposes semi-supervised projective non-negative matrix factorization method (Semi-PNMF) learn an effective classifier from both and unlabeled samples, thus boosting subsequent classification performance. In particular, Semi-PNMF jointly learns subspace concatenated indicates classes by positions maximum entries their coefficients. Because incorporates statistical information large volume learned subspace, it can more representative subspaces boost We developed multiplicative update rule (MUR) optimize proved its convergence. The experimental results two multiclass profile datasets show that outperforms methods.