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Sparse representation (SR) has been being applied as a state-of-the-art machine learning approach. Sparse representation classification (SRC1) approaches based on norm regularization and non-negative-least-squares (NNLS) classification approach based on non-negativity have been proposed to be powerful and robust. However, these approaches are extremely slow when the size of training samples is...
Non-negative information can benefit the analysis of microarray data. This paper investigates the classification performance of non-negative matrix factorization (NMF) over gene-sample data. We also extends it to higher-order version for classification of clinical time-series data represented by tensor. Experiments show that NMF and the higher-order NMF can achieve at least comparable prediction performance.
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