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Nonnegative matrix factorization with quadratic programming
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DOI：10.1016/j.neucom.2007.01.013.png)
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Abstract
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Nonnegative matrix factorization (NMF) solves the following problem: find such nonnegative matrices A is an element of R-+(IxJ) and X is an element of R-+(JxK) that Y congruent to AX, given only Y is an element of R-IxK and the assigned index J (K >> 1 >= J). Basically, the factorization is achieved by alternating minimization of a given cost function subject to nonnegativity constraints. In the paper, we propose to use quadratic programming (QP) to solve the minimization problems. The Tikhonov regularized squared Euclidean cost function is extended with a logarithmic barrier function (which satisfies nonnegativity constraints), and then using second-order Taylor expansion, a QP problem is formulated. This problem is solved with some trust-region subproblem algorithm. The numerical tests are performed on the blind source separation problems. (C) 2008 Elsevier B.V. All rights reserved.
Keywords:
nonnegative matrix factorization
blind source separation
quadratic programming
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