کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
416469 681370 2012 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Selecting the number of components in principal component analysis using cross-validation approximations
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Selecting the number of components in principal component analysis using cross-validation approximations
چکیده انگلیسی

Cross-validation is a tried and tested approach to select the number of components in principal component analysis (PCA), however, its main drawback is its computational cost. In a regression (or in a non parametric regression) setting, criteria such as the general cross-validation one (GCV) provide convenient approximations to leave-one-out cross-validation. They are based on the relation between the prediction error and the residual sum of squares weighted by elements of a projection matrix (or a smoothing matrix). Such a relation is then established in PCA using an original presentation of PCA with a unique projection matrix. It enables the definition of two cross-validation approximation criteria: the smoothing approximation of the cross-validation criterion (SACV) and the GCV criterion. The method is assessed with simulations and gives promising results.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 56, Issue 6, June 2012, Pages 1869–1879
نویسندگان
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