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posted on 2019-03-13, 17:38 authored by Vincent Guillemot, Derek Beaton, Arnaud Gloaguen, Tommy Löfstedt, Brian Levine, Nicolas Raymond, Arthur Tenenhaus, Hervé Abdi

PCA with the reduced set of 14 items from the OSIQ. Left: Dimensions 1 and 2 for plain PCA; Right: Dimensions 1 and 2 after a two-dimensional Varimax rotation.

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A constrained singular value decomposition method that integrates sparsity and orthogonality
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projectionvariableorthogonalityCSVDsparsification methodvalue decomposition methodalgorithmconstraintnorms L 1value decompositionimplement

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