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Algorithms for Non-negative Matrix Factorization Daniel D. Lee£ £ Bell Laboratories Lucent Technologies Murray Hill, NJ 07974 H. Sebastian Seung£à à Dept. of Brain and Cog. Sci. Massachusetts Institute of Technology Cambridge, MA 02138 Abstract Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition for multivariate data. Two different multiplicative algori
lucille 開発日記: non-Negative Matrix Factorization
non-Negative Matrix Factorization 今年㮠SIGGRAPH 2004 ã®è«–æ–‡ã€Efficient BRDF Importance Sampling Using A Factored Representation ã§ã¯ã€ãƒ‡ãƒ¼ã‚¿ã‚’圧縮ã€ã‚¤ãƒ³ãƒãƒ¼ã‚¿ãƒ³ã‚¹ã‚µãƒ³ãƒ—ルã—ã‚„ã™ã„よã†ã«ã€äºŒæ¬¡å…ƒã®è¡Œåˆ—ã‚’ 2 ã¤ã®ã‚ˆã‚Šè¦ç´ æ•°ã®å°‘ãªã„二次元ã®è¡Œåˆ—ã®ç©ã«åˆ†è§£( Y = GF ã®ã‚ˆã†ã«ã€ ãŸã¨ãˆã° 4x4 行列を〠4x1 㨠1x4 ã® 2 ã¤ã®è¡Œåˆ—ã«åˆ†è§£ ) ã—ã¦ã„ã‚‹ã®ã§ã™ãŒã€ã“ã®ã¨ãéžè² 値行列分解(non-negative matrix factorization, NMF)ã¨å‘¼ã°ã‚Œã‚‹æ‰‹æ³•ã‚’用ã„ã¦ã„ã¾ã™ã€‚ NMF ã¯ã€åå‰ã®é€šã‚Šè² 値ã®è¦ç´ ã®ãªã„行列をã€åˆ†è§£å¾Œã®è¡Œåˆ—ã‚‚ã¾ãŸè² 値ã®è¦ç´ ã‚’æŒãŸãªã„よã†ã™ã‚‹æ‰‹æ³•ã§ã™ã€‚特異値分解(singular value decomposition,
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