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.
We propose a new type of regularization approach that encourages non-overlapness in representation learning, for the sake of improving interpretability and reducing overfitting. The paper introduces a matrix regularizer to simultaneously induce both sparsity and approximate orthogonality. The paper studies a regularization method to promote sparsity and reduce the overlap among the supports of the weight vectors in the learned representations to enhance interpretability and avoid overfitting The paper proposed a new regularization approach that simultaneously encourages the weight vectors (W) to be sparse and orthogonal to each other.