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- W2146111570 abstract "Recently there has developed considerable interest in using sparseness with PCA. Almost all previous methods concentrate on zeroing out some loadings. Here we develop a new approach which zeros out whole variables automatically. We formulate a vector l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> penalized PCA criterion and optimize it by steepest descent along geodesic on a Grassman manifold. This ensures that each step obeys PCA orthogonality as well as an invariance property of the criterion. We show in simulations that it outperforms a previous svPCA algorithm and apply it to a real high dimensional functional Magnetic Resonance Imaging (fMRI) data." @default.
- W2146111570 created "2016-06-24" @default.
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- W2146111570 date "2009-04-01" @default.
- W2146111570 modified "2023-09-25" @default.
- W2146111570 title "Sparse variable PCA using a steepest descent on a Grassman manifold" @default.
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- W2146111570 doi "https://doi.org/10.1109/icassp.2009.4960318" @default.
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