Abstract
The non-negative matrix factorization (NMF) aims to find two matrix factors for a matrix X such that X ≈ W H, where W and H are both nonnegative matrices. The non-negativity constraint arises often naturally in applications in physics and engineering. In this paper, we propose a new NMF approach, which incorporates sparseness constraints explicitly. The new model can learn much sparser matrix factorization. Also, an objective function is defined to impose the sparseness constraint, in addition to the non-negative constraint. Experimental results on two document datasets show the effectiveness and efficiency of the proposed method.
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