Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity
Data Structures and Algorithms
2015-06-22 v3 Machine Learning
Machine Learning
Abstract
We present a simple, general technique for reducing the sample complexity of matrix and tensor decomposition algorithms applied to distributions. We use the technique to give a polynomial-time algorithm for standard ICA with sample complexity nearly linear in the dimension, thereby improving substantially on previous bounds. The analysis is based on properties of random polynomials, namely the spacings of an ensemble of polynomials. Our technique also applies to other applications of tensor decompositions, including spherical Gaussian mixture models.
Cite
@article{arxiv.1412.2954,
title = {Max vs Min: Tensor Decomposition and ICA with nearly Linear Sample Complexity},
author = {Santosh S. Vempala and Ying Xiao},
journal= {arXiv preprint arXiv:1412.2954},
year = {2015}
}