title: Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification.
abstract: Advances in DNA microarray technologies have made gene expression profiles a significant candidate in identifying different types of cancers. Traditional learning-based cancer identification methods utilize labeled samples to train a classifier, but they are inconvenient for practical application because labels are quite expensive in the clinical cancer research community. This paper proposes a semi-supervised projective non-negative matrix factorization method (Semi-PNMF) to learn an effective classifier from both labeled and unlabeled samples, thus boosting subsequent cancer classification performance. In particular, Semi-PNMF jointly learns a non-negative subspace from concatenated labeled and unlabeled samples and indicates classes by the positions of the maximum entries of their coefficients. Because Semi-PNMF incorporates statistical information from the large volume of unlabeled samples in the learned subspace, it can learn more representative subspaces and boost classification performance. We developed a multiplicative update rule (MUR) to optimize Semi-PNMF and proved its convergence. The experimental results of cancer classification for two multiclass cancer gene expression profile datasets show that Semi-PNMF outperforms the representative methods.
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China; National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha 410073, China.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China; National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha 410073, China.
authors.forename: Naiyang
authors.initials: N
authors.lastname: Guan
authors.affiliation: Department of Chemistry and Biology, College of Science, National University of Defense Technology, Changsha, Hunan, China.
authors.forename: Zhilong
authors.initials: Z
authors.lastname: Jia
authors.affiliation: College of Information System and Management, National University of Defense Technology, Changsha, Hunan, 410073 China.
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Qiu
authors.affiliation: College of Computer, National University of Defense Technology, Changsha 410073, China; National Laboratory for Parallel and Distributed Processing, National University of Defense Technology, Changsha 410073, China.
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Luo
mesh_terms: D000208:Acute Disease; D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D015973:Gene Expression Regulation, Leukemic; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000235:genetics; D009369:Neoplasms / Q000145:classification*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results