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• W2892093392 abstract "We consider the problem of updating the SVD when augmenting a “tall thin” matrix, i.e., a rectangular matrix A∈Rm×n with m≫n. Supposing that an SVD of A is already known, and given a matrix B∈Rm×n′, we derive an efficient method to compute and efficiently store the SVD of the augmented matrix [AB]∈Rm×(n+n′). This is an important tool for two types of applications: in the context of principal component analysis, the dominant left singular vectors provided by this decomposition form an orthonormal basis for the best linear subspace of a given dimension, while from the right singular vectors one can extract an orthonormal basis of the kernel of the matrix. We also describe two concrete applications of these concepts which motivated the development of our method and to which it is very well adapted." @default.
• W2892093392 created "2018-09-27" @default.
• W2892093392 creator A5050930736 @default.
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• W2892093392 date "2019-01-01" @default.
• W2892093392 modified "2023-10-02" @default.
• W2892093392 title "SVD update methods for large matrices and applications" @default.
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• W2892093392 doi "https://doi.org/10.1016/j.laa.2018.09.014" @default.
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