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• W2221874034 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." @default.
• W2221874034 created "2016-06-24" @default.
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• W2221874034 date "2015-09-22" @default.
• W2221874034 modified "2023-10-06" @default.
• W2221874034 title "Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification" @default.
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• W2221874034 doi "https://doi.org/10.1371/journal.pone.0138814" @default.
• W2221874034 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4579132" @default.
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