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

@inproceedings{Zhang2015SemiSupervisedPN,
  title={Semi-Supervised Projective Non-Negative Matrix Factorization for Cancer Classification},
  author={Xiang Zhang and Naiyang Guan and Zhilong Jia and Xiaogang Qiu and Zhigang Luo and Ramin Homayouni},
  booktitle={PloS one},
  year={2015}
}
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… CONTINUE READING
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