Robust Sparse Coding via Self-Paced Learning
This addresses robustness issues in sparse coding for signal processing applications, but it appears incremental as it builds on existing self-paced learning methods.
The paper tackles the problem of sparse coding algorithms getting stuck in bad local minima due to outliers and noisy data by proposing a Self-Paced Sparse Coding framework that gradually includes matrix elements from easy to complex, showing efficacy in experiments on real-world data.
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.