The adaptive block sparse PCA and its application to multi-subject FMRI data analysis using sparse mCCA
Abd-Krim Seghouane, Asif Iqbal
SIGNAL PROCESSING | ELSEVIER SCIENCE BV | Published : 2018
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 ..View full abstract
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Awarded by Australian Research Council
This work was supported by the Australian Research Council through Grant FT. 130101394.