Sparse Matrix Factorization

Behnam Neyshabur; Rina Panigrahy

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arXiv · 1311.3315

Sparse Matrix Factorization

Behnam Neyshabur·Rina Panigrahy

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Abstract

We investigate the problem of factorizing a matrix into several sparse matrices and propose an algorithm for this under randomness and sparsity assumptions. This problem can be viewed as a simplification of the deep learning problem where finding a factorization corresponds to finding edges in different layers and values of hidden units. We prove that under certain assumptions for a sparse linear deep network with $n$ nodes in each layer, our algorithm is able to recover the structure of the network and values of top layer hidden units for depths up to $\tilde O(n^{1/6})$. We further discuss the relation among sparse matrix factorization, deep learning, sparse recovery and dictionary learning.

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Behnam Neyshabur, Rina Panigrahy. 2014-05-13. Sparse Matrix Factorization. https://arxiv.org/abs/1311.3315

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