Kernel-PCAã®explained_variance_ratioを計算scikit-learnã®PCA（主æˆåˆ†åˆ†æžï¼‰ã«ã¯explained_variance_ratio_ã¨ã„ã†ã€æ¬¡å…ƒã‚’削減ã—ãŸã“ã¨ã§ã©ã®ç¨‹åº¦åˆ†æ•£ï¼ˆãƒ‡ãƒ¼ã‚¿ã‚’説明ã§ãる度åˆã„）ãŒè½ã¡ãŸã®ã‹ã‚’ç°¡å˜ã«ç¢ºèªã§ãる値ãŒã‚ã‚Šã¾ã™ã€‚Kernel-PCAã§ã¯ã‚«ãƒ¼ãƒãƒ«ãƒˆãƒªãƒƒã‚¯ã«ã‚ˆã‚Šã€ç‰¹å¾´é‡ã®ç©ºé–“ãŒå¤‰ã‚ã£ã¦ã—ã¾ã†ã®ã§ã€explained_variance_ratio_ã¨ã„ã†ãƒ‘ラメーターã¯å˜åœ¨ã—ã¾ã›ã‚“。ãŸã ã“ã®å€¤ã¯ãƒã‚¤ãƒ‘ーパラメータã®ãƒãƒ¥ãƒ¼ãƒ‹ãƒ³ã‚°ç”¨ã«ã¨ã¦ã‚‚便利ãªã®ã§ã€èª¬æ˜Žåˆ†æ•£æ¯”を擬似的ã«æ±‚ã‚る方法を書ãã¾ã™ã€‚ 主æˆåˆ†åˆ†æžï¼ˆPCA）ã®èª¬æ˜Žåˆ†æ•£æ¯” 主æˆåˆ†åˆ†æžã®èª¬æ˜Žåˆ†æ•£æ¯”ã¯PCA.explained_variance_ratio_ã§ç°¡å˜ã«æ±‚ã‚ã‚‹ã“ã¨ãŒã§ãã¾ã™ãŒã€ã©ã®ã‚ˆã†ãªè¨ˆç®—ã‚’ã—ã¦ã„ã‚‹ã‹ç¢ºèªã™ã‚‹ãŸã‚ã«Numpyã§æ›¸ã„ã¦ã¿ã¾ã™ã€‚Irisデータセットを使ã„ã¾ã™ã€‚ import numpy as np from sklearn.
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Comparison of LDA and PCA 2D projection of Iris dataset
How to reverse PCA and reconstruct original variables from several principal components?
主æˆåˆ†åˆ†æžPCAを用ã„ã¦æ‰‹æ›¸ãæ•°å—を分æžã™ã‚‹ã€‚ãã®ï¼’ - Qiita
[PDF]カーãƒãƒ«æ³•å…¥é–€ 1． - 統計数ç†ç ”究所
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Pythonã§ä¸»æˆåˆ†åˆ†æž - old school magic
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