کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
564938 875658 2007 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian estimation of the number of principal components
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
Bayesian estimation of the number of principal components
چکیده انگلیسی

Recently, the technique of principal component analysis (PCA) has been expressed as the maximum likelihood solution for a generative latent variable model. A central issue in PCA is choosing the number of principal components to retain. This can be considered as a problem of model selection. In this paper, the probabilistic reformulation of PCA is used as a basis for a Bayesian approach of PCA to derive a model selection criterion for determining the true dimensionality of data. The proposed criterion is similar to the Bayesian Information Criterion, BIC, with a particular goodness of fit term and it is consistent. A simulation example that illustrate its performance for the determination of the number of principal components to be retained is presented.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 87, Issue 3, March 2007, Pages 562–568
نویسندگان
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