Robust Dictionary based Data Representation

TL;DR

This paper introduces a robust dictionary-based data representation method that effectively handles gross corruptions by enforcing zero coefficients for corrupted samples, improving the quality of clean data representation.

Contribution

It proposes a novel approach that distinguishes clean from corrupted samples via coefficient constraints, enhancing robustness in linear data representation.

Findings

The method effectively isolates corrupted samples.

It improves representation quality for clean data.

The approach has potential for future applications.

Abstract

The robustness to noise and outliers is an important issue in linear representation in real applications. We focus on the problem that samples are grossly corrupted, which is also the 'sample specific' corruptions problem. A reasonable assumption is that corrupted samples cannot be represented by the dictionary while clean samples can be well represented. This assumption is enforced in this paper by investigating the coefficients of corrupted samples. Concretely, we require the coefficients of corrupted samples be zero. In this way, the representation quality of clean data can be assured without the effect of corrupted data. At last, a robust dictionary based data representation approach and its sparse representation version are proposed, which have directive significance for future applications.