| Budget Amount *help |
¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2015: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2014: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Outline of Final Research Achievements |
This study investigated novel methods to improve the accuracy of nonnegative matrix factorization (NMF) from both theoretical and experimental side. Theoretically, it has shown the equivalence between information-theoretic clustering (ITC) and NMF based on generalized KL divergence. Then, it proposed a novel initialization method for NMF using ITC and experimentally showed the effectiveness of the method compared to conventional methods. It also proposed another algorithm for NMF using competitive learning which selects a subset of vectors as winner and showed the effectiveness.
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