Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing

Details Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing Sparse and Unique Nonnegative Matrix Factorization Through Data Preprocessing

2012-04-11

arxiv.org/abs/1204.2436v1

Statistics

Machine Learning

34 pages, 11 figures

Scientific paper

Nonnegative matrix factorization (NMF) has become a very popular technique in machine learning because it automatically extracts meaningful features through a sparse and part-based representation. However, NMF has the drawback of being highly ill-posed, that is, there typically exist many different but equivalent factorizations. In this paper, we introduce a completely new way to obtaining more well-posed NMF problems whose solutions are sparser. Our technique is based on the preprocessing of the nonnegative input data matrix, and relies on the theory of M-matrices and the geometric interpretation of NMF. This approach provably leads to optimal and sparse solutions under the separability assumption of Donoho and Stodden (NIPS, 2003), and, for rank-three matrices, makes the number of exact factorizations finite. We illustrate the effectiveness of our technique on several image datasets.

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