Restricted Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering
نویسندگان
چکیده
منابع مشابه
Sparse Subspace Clustering by Orthogonal Matching Pursuit
Subspace clustering methods based on `1, `2 or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical success. However, the choice of the regularizer can greatly impact both theory and practice. For instance, `1 regularization is guaranteed to give a subspace-preserving affinity (i.e., there are no connections between points from diffe...
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This paper is concerned with the performance of Orthogonal Matching Pursuit (OMP) algorithms applied to a dictionary D in a Hilbert space H. Given an element f ∈ H, OMP generates a sequence of approximations fn, n = 1, 2, . . ., each of which is a linear combination of n dictionary elements chosen by a greedy criterion. It is studied whether the approximations fn are in some sense comparable to...
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We shall show that if the restricted isometry constant (RIC) δs+1(A) of the measurement matrix A satisfies δs+1(A) < 1 √ s+ 1 , then the greedy algorithm Orthogonal Matching Pursuit(OMP) will succeed. That is, OMP can recover every s-sparse signal x in s iterations from b = Ax. Moreover, we shall show the upper bound of RIC is sharp in the following sense. For any given s ∈ N, we shall construc...
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ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2019
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2019.2953638