Statistics > Machine Learning
[Submitted on 17 Feb 2015 (v1), last revised 19 Feb 2015 (this version, v2)]
Title:A New Sampling Technique for Tensors
View PDFAbstract:In this paper we propose new techniques to sample arbitrary third-order tensors, with an objective of speeding up tensor algorithms that have recently gained popularity in machine learning. Our main contribution is a new way to select, in a biased random way, only $O(n^{1.5}/\epsilon^2)$ of the possible $n^3$ elements while still achieving each of the three goals: \\ {\em (a) tensor sparsification}: for a tensor that has to be formed from arbitrary samples, compute very few elements to get a good spectral approximation, and for arbitrary orthogonal tensors {\em (b) tensor completion:} recover an exactly low-rank tensor from a small number of samples via alternating least squares, or {\em (c) tensor factorization:} approximating factors of a low-rank tensor corrupted by noise. \\ Our sampling can be used along with existing tensor-based algorithms to speed them up, removing the computational bottleneck in these methods.
Submission history
From: Srinadh Bhojanapalli [view email][v1] Tue, 17 Feb 2015 20:23:13 UTC (731 KB)
[v2] Thu, 19 Feb 2015 21:05:53 UTC (731 KB)
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