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Reports (Research Report) Year : 2017

A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm

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Abstract

We consider the problem of learning a high-dimensional but low-rank matrix from a large-scale dataset distributed over several machines, where low-rankness is enforced by a convex trace norm constraint. We propose DFW-Trace, a distributed Frank-Wolfe algorithm which leverages the low-rank structure of its updates to achieve efficiency in time, memory and communication usage. The step at the heart of DFW-Trace is solved approximately using a distributed version of the power method. We provide a theoretical analysis of the convergence of DFW-Trace, showing that we can ensure sublinear convergence in expectation to an optimal solution with few power iterations per epoch. We implement DFW-Trace in the Apache Spark distributed programming framework and validate the usefulness of our approach on synthetic and real data, including the ImageNet dataset with high-dimensional features extracted from a deep neural network.
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Dates and versions

hal-01672066 , version 1 (23-12-2017)

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Wenjie Zheng, Aurélien Bellet, Patrick Gallinari. A Distributed Frank-Wolfe Framework for Learning Low-Rank Matrices with the Trace Norm. [Research Report] Inria Lille. 2017, pp.1-19. ⟨hal-01672066⟩
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