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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Robust principal component analysis ： ウィキペディア英語版 Robust principal component analysis Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure Principal component analysis (PCA) which works well with respect to ''grossly'' corrupted observations. A number of different approaches exist for Robust PCA, including an idealized version of Robust PCA, which aims to recover a low-rank matrix L0 from highly corrupted measurements M = L0 +S0.〔 This decomposition in low-rank and sparse matrices can be achieved by techniques such as Principal Component Pursuit method (PCP),〔 Stable PCP, Quantized PCP , Block based PCP, and Local PCP. Then, optimization methods are used such as the Augmented Lagrange Multiplier Method (ALM), Alternating Direction Method (ADM), Fast Alternating Minimization (FAM) or Iteratively Reweighted Least Squares (IRLS ). Bouwmans and Zahzah have made a complete survey 〔 in 2014. ==Algorithms== 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Robust principal component analysis」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.