Principal components adjusted variable screening
نویسندگان
چکیده
منابع مشابه
Interference and noise-adjusted principal components analysis
The goal of principal components analysis (PCA) is to find principal components in accordance with maximum variance of a data matrix. However, it has been shown recently that such variance-based principal components may not adequately represent image quality. As a result, a modified PCA approach based on maximization of SNR was proposed. Called maximum noise fraction (MNF) transformation or noi...
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Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal distributions. However, it is well known that the asymptotic chi-square approximation for the cutoff value of the Mahalanobis distance based on several robust estimates (l...
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Note that SPSS does not provide statistical significance tests for any of the estimated parameters (such as loadings), nor does it provide confidence intervals. Judgments about the adequacy of a oneor two-component model are not made based on statistical significance tests, but by making arbitrary judgments whether the model that is limited to just one or two components does an adequate job of ...
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We consider the online version of the well known Principal Component Analysis (PCA) problem. In standard PCA, the input to the problem is a set of ddimensional vectors X = [x1, . . . ,xn] and a target dimension k < d; the output is a set of k-dimensional vectors Y = [y1, . . . ,yn] that minimize the reconstruction error: minΦ ∑ i ‖xi − Φyi‖2. Here, Φ ∈ Rd×k is restricted to being isometric. The...
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ژورنال
عنوان ژورنال: Computational Statistics & Data Analysis
سال: 2017
ISSN: 0167-9473
DOI: 10.1016/j.csda.2016.12.015