Learning Less-Overlapping Representations

Hongbao Zhang, Pengtao Xie, Eric Xing

Abstract: In representation learning (RL), how to make the learned representations easy to interpret and less overfitted to training data are two important but challenging issues. To address these problems, we study a new type of regularization approach that encourages the supports of weight vectors in RL models to have small overlap, by simultaneously promoting near-orthogonality among vectors and sparsity of each vector. We apply the proposed regularizer to two models: neural networks (NNs) and sparse coding (SC), and develop an efficient ADMM-based algorithm for regularized SC. Experiments on various datasets demonstrate that weight vectors learned under our regularizer are more interpretable and have better generalization performance.

TL;DR: We propose a new type of regularization approach that encourages non-overlapness in representation learning, for the sake of improving interpretability and reducing overfitting.

Keywords: Less-overlapness, regularization, near-orthogonality, sparsity

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1711.09300/code)