Improving the generalization via coupled tensor norm regularization

Ying Gao; Yunfei Qu; Chunfeng Cui; Deren Han

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arXiv · 2302.11780

Improving the generalization via coupled tensor norm regularization

Ying Gao·Yunfei Qu·Chunfeng Cui·Deren Han

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Abstract

In this paper, we propose a coupled tensor norm regularization that could enable the model output feature and the data input to lie in a low-dimensional manifold, which helps us to reduce overfitting. We show this regularization term is convex, differentiable, and gradient Lipschitz continuous for logistic regression, while nonconvex and nonsmooth for deep neural networks. We further analyze the convergence of the first-order method for solving this model. The numerical experiments demonstrate that our method is efficient.

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Ying Gao, Yunfei Qu, Chunfeng Cui, Deren Han. 2023-02-23. Improving the generalization via coupled tensor norm regularization. https://arxiv.org/abs/2302.11780

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