WebJun 12, 2013 · Matrix completion, i.e., the exact and provable recovery of a low-rank matrix from a small subset of its elements, is currently only known to be possible if the matrix … Web(2015) Chen et al. Journal of Machine Learning Research. Matrix completion, i.e., the exact and provable recovery of a low-rank matrix from a small subset of its elements, is currently only known to be possible if the matrix satisfies a restrictive structural constraint-known as incoherence-on it...
Completing Any Low-rank Matrix, Provably - NASA/ADS
WebAug 1, 2024 · Theorem 1 shows that observing entries according to this relaxed leverage score sampling in (3), we can recover any m × n matrix of rank ϱ exactly from Θ ( ( ( m + n) ϱ − ϱ 2) log 2 ( m + n)) observed entries, via (1). This bound on the sample size is optimal (up to log 2 ( m + n) factor) in the number of degrees of freedom of a rank- ϱ ... WebLow-rank matrix completion problem Given some entries of a matrix M, exactly recover (\complete") hidden entries I Assumption to make well-posed: M has low rank I M 2Rn n … shows like hensuki
Adaptive sampling in matrix completion: When can it help?
WebDec 1, 2024 · The problem of completing a large low rank matrix using a subset of revealed entries has received much attention in the last ten years. The main result of this paper gives a necessary and sufficient condition, stated in the language of graph limit theory, for a sequence of matrix completion problems with arbitrary missing patterns to … WebFeb 13, 2024 · Provable Low Rank Phase Retrieval. We study the Low Rank Phase Retrieval (LRPR) problem defined as follows: recover an matrix of rank from a different and independent set of phaseless (magnitude-only) linear projections of each of its columns. To be precise, we need to recover from when the measurement matrices are mutually … WebDec 11, 2024 · Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview (2024) Harnessing Structures in Big Data via Guaranteed Low-Rank Matrix Estimation (2024) Non-convex Optimization for Machine Learning (2024) Software. NCVX–a general-purpose optimization package for nonconvex, particularly constrained and … shows like helstrom