SUPERVISED NMF AS A SPARSE OPTIMIZATION PROBLEM
スポンサーリンク
概要
- 論文の詳細を見る
In this paper, we propose a novel scheme to supervised nonnegative matrix factorization (NMF). We formulate the supervised NMF as a sparse optimization problem assuming the availability of a set of basis vectors, some of which are irrelevant to a given matrix to be decomposed. The number of basis vectors to be actively used is obtained as a consequence of optimization. We present a state-of-the-art convex-analytic iterative solver which ensures global convergence. Simulation results show the efficacy of the proposed scheme in the case of perfect basis matrix.
- 2013-03-07
著者
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Yukawa Masahiro
Dept. Communications And Integrated Systems Tokyo Institute Of Technology
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Yukawa Masahiro
Dept. Electrical and Electronic Engineering, Niigata University
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MORIKAWA Yu
Dept. Electrical and Electronic Engineering, Niigata University
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- SUPERVISED NMF AS A SPARSE OPTIMIZATION PROBLEM
- SUPERVISED NMF AS A SPARSE OPTIMIZATION PROBLEM
- SUPERVISED NMF AS A SPARSE OPTIMIZATION PROBLEM