Learning Sparse Basis Vectors in Small-Sample Datasets through Regularized Non-Negative Matrix Factorization
نویسندگان
چکیده
This article presents a novel dimensionality-reduction technique, Regularized Non-negative Matrix Factorization (RNMF), which combines the non-negativity constraint of NMF with a regularization term. In contrast with NMF, which degrades to holistic representations with decreasing amount of data, RNMF is able to extract parts of objects even in the small-sample case.
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