Semi-supervised Dictionary Learning Based on Hilbert-Schmidt Independence Criterion

TL;DR

This paper introduces a semi-supervised dictionary learning method that maximizes data-label dependency using HSIC and efficiently incorporates unlabeled data to improve classification accuracy.

Contribution

It proposes a novel semi-supervised dictionary learning algorithm with closed-form solutions, enabling fast learning and improved performance by leveraging both labeled and unlabeled data.

Findings

Enhanced classification accuracy on benchmark datasets.

Effective utilization of unlabeled data improves performance.

Fast closed-form solution for dictionary and sparse coefficients.

Abstract

In this paper, a novel semi-supervised dictionary learning and sparse representation (SS-DLSR) is proposed. The proposed method benefits from the supervisory information by learning the dictionary in a space where the dependency between the data and class labels is maximized. This maximization is performed using Hilbert-Schmidt independence criterion (HSIC). On the other hand, the global distribution of the underlying manifolds were learned from the unlabeled data by minimizing the distances between the unlabeled data and the corresponding nearest labeled data in the space of the dictionary learned. The proposed SS-DLSR algorithm has closed-form solutions for both the dictionary and sparse coefficients, and therefore does not have to learn the two iteratively and alternately as is common in the literature of the DLSR. This makes the solution for the proposed algorithm very fast. The experiments confirm the improvement in classification performance on benchmark datasets by including the information from both labeled and unlabeled data, particularly when there are many unlabeled data.