Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix

• URL: http://arxiv.org/abs/2003.07898v1
• Date: Tue, 17 Mar 2020 19:12:21 GMT
• Title: Statistically Guided Divide-and-Conquer for Sparse Factorization of Large Matrix
• Authors: Kun Chen, Ruipeng Dong, Wanwan Xu, Zemin Zheng
• Abstract summary: We formulate the statistical problem as a sparse factor regression and tackle it with a divide-conquer approach. In the first stage division, we consider both latent parallel approaches for simplifying the task into a set of co-parsesparserank estimation (CURE) problems. In the second stage division, we innovate a stagewise learning technique, consisting of a sequence simple incremental paths, to efficiently trace out the whole solution of CURE.
• Score: 2.345015036605934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The sparse factorization of a large matrix is fundamental in modern statistical learning. In particular, the sparse singular value decomposition and its variants have been utilized in multivariate regression, factor analysis, biclustering, vector time series modeling, among others. The appeal of this factorization is owing to its power in discovering a highly-interpretable latent association network, either between samples and variables or between responses and predictors. However, many existing methods are either ad hoc without a general performance guarantee, or are computationally intensive, rendering them unsuitable for large-scale studies. We formulate the statistical problem as a sparse factor regression and tackle it with a divide-and-conquer approach. In the first stage of division, we consider both sequential and parallel approaches for simplifying the task into a set of co-sparse unit-rank estimation (CURE) problems, and establish the statistical underpinnings of these commonly-adopted and yet poorly understood deflation methods. In the second stage of division, we innovate a contended stagewise learning technique, consisting of a sequence of simple incremental updates, to efficiently trace out the whole solution paths of CURE. Our algorithm has a much lower computational complexity than alternating convex search, and the choice of the step size enables a flexible and principled tradeoff between statistical accuracy and computational efficiency. Our work is among the first to enable stagewise learning for non-convex problems, and the idea can be applicable in many multi-convex problems. Extensive simulation studies and an application in genetics demonstrate the effectiveness and scalability of our approach.

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