Robust Sparse Coding via Self-Paced Learning

TL;DR

This paper introduces Self-Paced Sparse Coding (SPSC), a robust framework that gradually incorporates data complexity to improve sparse coding performance, especially in noisy and outlier-prone scenarios.

Contribution

It proposes a unified, self-paced learning framework for sparse coding that dynamically selects samples, features, and elements to enhance robustness against noise and outliers.

Findings

Effective in handling noisy and outlier data

Demonstrates improved robustness over traditional methods

Validated on real-world datasets

Abstract

Sparse coding (SC) is attracting more and more attention due to its comprehensive theoretical studies and its excellent performance in many signal processing applications. However, most existing sparse coding algorithms are nonconvex and are thus prone to becoming stuck into bad local minima, especially when there are outliers and noisy data. To enhance the learning robustness, in this paper, we propose a unified framework named Self-Paced Sparse Coding (SPSC), which gradually include matrix elements into SC learning from easy to complex. We also generalize the self-paced learning schema into different levels of dynamic selection on samples, features and elements respectively. Experimental results on real-world data demonstrate the efficacy of the proposed algorithms.