Low Rank Approximation - Algorithms, Implementation, Applications
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منابع مشابه
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The problem of minimizing the rank of a matrix subject to affine constraints has many applications in machine learning, and is known to be NP-hard. One of the tractable relaxations proposed for this problem is nuclear norm (or trace norm) minimization of the matrix, which is guaranteed to find the minimum rank matrix under suitable assumptions. In this paper, we propose a family of Iterative Re...
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We consider the synthesis problem of Compressed Sensing –given s and an M×n matrix A, extract from it an m × n submatrix Am, certified to be s-good, with m as small as possible. Starting from the verifiable sufficient conditions of s-goodness, we express the synthesis problem as the problem of approximating a given matrix by a matrix of specified low rank in the uniform norm. We propose randomi...
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تاریخ انتشار 2012