Sparse Coding with Outliers

Abstract

Sparse coding is invalid to learn parts-based representations when data is corrupted by outliers. In this paper, matrix completion is considered into sparse coding to handle outliers and a novel sparse coding method is proposed to learn a robust subspace. Experiments on the ORL dataset with salt and pepper noise and contiguous occlusion demonstrate that our proposed sparse method is more effective and robust in achieving a robust subspace.