Unsupervised Feature Selection Using Nonnegative Spectral Analysis

TLDR

The paper proposes a new unsupervised learning algorithm, Nonnegative Discriminative Feature Selection (NDFS). NDFS jointly learns cluster labels and a sparse feature‑selection matrix by applying spectral clustering with nonnegative class indicators and an l2,1‑norm regularizer, optimized via an efficient iterative algorithm. Experiments on real datasets show that NDFS selects more discriminative features and outperforms existing methods.

Abstract

In this paper, a new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), is proposed. To exploit the discriminative information in unsupervised scenarios, we perform spectral clustering to learn the cluster labels of the input samples, during which the feature selection is performed simultaneously. The joint learning of the cluster labels and feature selection matrix enables NDFS to select the most discriminative features. To learn more accurate cluster labels, a nonnegative constraint is explicitly imposed to the class indicators. To reduce the redundant or even noisy features, l2,1-norm minimization constraint is added into the objective function, which guarantees the feature selection matrix sparse in rows. Our algorithm exploits the discriminative information and feature correlation simultaneously to select a better feature subset. A simple yet efficient iterative algorithm is designed to optimize the proposed objective function. Experimental results on different real world datasets demonstrate the encouraging performance of our algorithm over the state-of-the-arts.