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- W2088338122 abstract "Given a set of data W = { w 1 , … , w N } ∈ R D drawn from a union of subspaces, we focus on determining a nonlinear model of the form U = ⋃ i ∈ I S i , where { S i ⊂ R D } i ∈ I is a set of subspaces, that is nearest to W . The model is then used to classify W into clusters. Our approach is based on the binary reduced row echelon form of data matrix, combined with an iterative scheme based on a non-linear approximation method. We prove that, in absence of noise, our approach can find the number of subspaces, their dimensions, and an orthonormal basis for each subspace S i . We provide a comprehensive analysis of our theory and determine its limitations and strengths in presence of outliers and noise." @default.
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- W2088338122 date "2014-09-01" @default.
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- W2088338122 title "Reduced row echelon form and non-linear approximation for subspace segmentation and high-dimensional data clustering" @default.
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- W2088338122 doi "https://doi.org/10.1016/j.acha.2013.12.001" @default.
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