کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6957031 1451914 2018 34 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
The adaptive block sparse PCA and its application to multi-subject FMRI data analysis using sparse mCCA
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
The adaptive block sparse PCA and its application to multi-subject FMRI data analysis using sparse mCCA
چکیده انگلیسی
Motivated by the problem of multi-subject functional magnetic resonance imaging (fMRI) data sets analysis using multiple-set canonical correlation analysis (mCCA), in this paper we propose a new variant of the principal component analysis (PCA) method, namely the adaptive block sparse PCA. It has the advantage to produce modified principal components with block sparse loadings. It is derived using penalized rank one matrix approximation where the penalty is introduced in the minimization problem to promote block sparsity of the loading vectors. An efficient algorithm is proposed for its computation. The effectiveness of the proposed method is illustrated on the problem of multi-subject fMRI data sets analysis using mCCA which is a generalization of canonical correlation analysis (CCA) to three or more sets of variables. This application is obtained by deriving the connection between mCCA and the singular value decomposition (SVD).
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 153, December 2018, Pages 311-320
نویسندگان
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