Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization

TL;DR

This paper introduces nonnegative restricted Boltzmann machines (NRBMs), enhancing parts-based data representation, interpretability, and model stability, demonstrated across image, text, and medical data applications.

Contribution

The paper proposes NRBMs, a novel variant of RBMs with nonnegative weights, improving parts-based discovery, interpretability, and stability in predictive modeling.

Findings

NRBMs produce interpretable parts-based representations comparable to NMF.

NRBMs improve stability of feature selection on medical data.

Features learned by NRBMs are more discriminative for classification.

Abstract

The success of any machine learning system depends critically on effective representations of data. In many cases, it is desirable that a representation scheme uncovers the parts-based, additive nature of the data. Of current representation learning schemes, restricted Boltzmann machines (RBMs) have proved to be highly effective in unsupervised settings. However, when it comes to parts-based discovery, RBMs do not usually produce satisfactory results. We enhance such capacity of RBMs by introducing nonnegativity into the model weights, resulting in a variant called nonnegative restricted Boltzmann machine (NRBM). The NRBM produces not only controllable decomposition of data into interpretable parts but also offers a way to estimate the intrinsic nonlinear dimensionality of data, and helps to stabilize linear predictive models. We demonstrate the capacity of our model on applications such as handwritten digit recognition, face recognition, document classification and patient readmission prognosis. The decomposition quality on images is comparable with or better than what produced by the nonnegative matrix factorization (NMF), and the thematic features uncovered from text are qualitatively interpretable in a similar manner to that of the latent Dirichlet allocation (LDA). The stability performance of feature selection on medical data is better than RBM and competitive with NMF. The learned features, when used for classification, are more discriminative than those discovered by both NMF and LDA and comparable with those by RBM.