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A Novel M-Estimator for Robust PCA
[article]
2014
arXiv
pre-print
We study the basic problem of robust subspace recovery. That is, we assume a data set that some of its points are sampled around a fixed subspace and the rest of them are spread in the whole ambient space, and we aim to recover the fixed underlying subspace. We first estimate "robust inverse sample covariance" by solving a convex minimization procedure; we then recover the subspace by the bottom eigenvectors of this matrix (their number correspond to the number of eigenvalues close to 0). We
arXiv:1112.4863v4
fatcat:hpee6ljbtvenhgd65ikqormru4