Singular value decomposition and principal component analysis
Citation: Wall, Michael E., Andreas Rechtsteiner, Luis M. Rocha."Singular value decomposition and principal component analysis". in A Practical Approach to Microarray Data Analysis. D.P. Berrar, W. Dubitzky, M. Granzow, eds. pp. 91-109, Kluwer: Norwell, MA (2003). LANL LA-UR-02-4001. Also available in the arXiv.org e-Print archive and in Adobe Acrobat (.pdf) format. Abstract. This chapter describe
TruncatedSVD
TruncatedSVD# class sklearn.decomposition.TruncatedSVD(n_components=2, *, algorithm='randomized', n_iter=5, n_oversamples=10, power_iteration_normalizer='auto', random_state=None, tol=0.0)[source]# Dimensionality reduction using truncated SVD (aka LSA). This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this estimato
ã™ã¹ã¦ãŒMFã«ãªã‚‹ - Fire and Motion
ã™ã¹ã¦ãŒFã«ãªã‚‹ï¼Œæ˜ åƒåŒ–ã™ã‚‹ã¿ãŸã„ã§ã™ã．犀å·å‰µå¹³ã‚‚西之園èŒçµµã‚‚é…å½¹ãŒã‚¤ãƒ¡ãƒ¼ã‚¸ã¨é•ã£ã¦ä¸€éƒ¨ã§è©±é¡Œã«ãªã£ã¦ã„ã¾ã—ãŸï¼Žã•ã¦ï¼Œæœ€è¿‘テンソル分解を使ã£ãŸè«–文をよã見ã‹ã‘ã‚‹ã®ã§ã™ãŒï¼Œã„ã¾ã ã«ãã¡ã‚“ã¨æ•´ç†ã§ããšã«ã„ã¾ã™ï¼Žãƒ†ãƒ³ã‚½ãƒ«ã‹ã‚ã„ã„よ，テンソル． ãã“ã§ï¼Œã¾ãšã¯è¡Œåˆ—分解(matrix factorization, matrix decomposition)ã‚’æ•´ç†ã—ã¦ã¿ã‚ˆã†ã¨æ€ã„ã¾ã™ï¼Žè¡Œåˆ—ã®åˆ†è§£æ‰‹æ³•ã¨ã„ã†ã¨ç·šå½¢ä»£æ•°çš„ãªè¦³ç‚¹ã‹ã‚‰ã¯ç°¡å˜ã«æ€ã„ã¤ãã ã‘ã§ã‚‚ 固有値分解 LU分解 コレスã‚ー分解 ãªã©ãŒã‚ã‚Šã¾ã™ãŒï¼Œã“れらã¯åˆ†è§£å‰ã®è¡Œåˆ—ã¨åˆ†è§£å¾Œã®è¡Œåˆ—ãŒä¸€è‡´ã™ã‚‹(ãŸã¨ãˆã°A=LU)方法ã§ã™ï¼Žä¸€æ–¹ã§ï¼Œæ©Ÿæ¢°å¦ç¿’やデータマイニング界隈（特ã«ãƒ¬ã‚³ãƒ¡ãƒ³ãƒ‡ãƒ¼ã‚·ãƒ§ãƒ³ç‰ï¼‰ã§å‡ºã¦ãる行列分解ã¨ã„ã†ã®ã¯ï¼Œå¤§è¦æ¨¡ãƒ‡ãƒ¼ã‚¿ã‚„関係性データã®ä¸ã‹ã‚‰ä½Žãƒ©ãƒ³ã‚¯ã®æ§‹é€ を抽出ã™ã‚‹ã“ã¨ã‚„次元圧縮を目的ã¨ã—ã¦ã„ã¾ã™ï¼Žãªã®ã§ï¼Œæ£ç¢ºã«è¨€ã†ãªã‚‰ã°ï¼Œè¡Œåˆ—分解ã¨
1
ランã‚ング
ランã‚ング
ランã‚ング
リリースã€éšœå®³æƒ…å ±ãªã©ã®ã‚µãƒ¼ãƒ“スã®ãŠçŸ¥ã‚‰ã›
最新ã®äººæ°—エントリーã®é…ä¿¡
処ç†ã‚’実行ä¸ã§ã™
j次ã®ãƒ–ックマーク
kå‰ã®ãƒ–ックマーク
lã‚ã¨ã§èªã‚€
eコメント一覧を開ã
oページを開ã
{{#tags}}- {{label}}
{{/tags}}