Understanding Deflation Process in Over-parametrized Tensor Decomposition

Rong Ge, Yunwei Ren, Xiang Wang, Mo Zhou

Keywords: tensor decomposition, overparametrization, gradient flow, implicit regularization

TL;DR: The paper analyzes gradient flow on overparametrized tensor decomposition and proves that its dynamics is similar to a tensor deflation process.

Abstract: In this paper we study the training dynamics for gradient flow on over-parametrized tensor decomposition problems. Empirically, such training process often first fits larger components and then discovers smaller components, which is similar to a tensor deflation process that is commonly used in tensor decomposition algorithms. We prove that for orthogonally decomposable tensor, a slightly modified version of gradient flow would follow a tensor deflation process and recover all the tensor components. Our proof suggests that for orthogonal tensors, gradient flow dynamics works similarly as greedy low-rank learning in the matrix setting, which is a first step towards understanding the implicit regularization effect of over-parametrized models for low-rank tensors.

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