Affine Non-negative Collaborative Representation Based Pattern Classification

TL;DR

This paper introduces the affine non-negative collaborative representation (ANCR) model, enhancing pattern classification by adding regularization and affine constraints to improve stability and data representation in affine subspaces.

Contribution

The paper proposes ANCR, a novel model that incorporates regularization and affine constraints, addressing limitations of previous NRC methods in pattern classification.

Findings

ANCR outperforms existing methods on benchmark datasets.

Regularization improves solution stability.

Affine constraints better model data in affine subspaces.

Abstract

During the past decade, representation-based classification methods have received considerable attention in pattern recognition. In particular, the recently proposed non-negative representation based classification (NRC) method has been reported to achieve promising results in a wide range of classification tasks. However, NRC has two major drawbacks. First, there is no regularization term in the formulation of NRC, which may result in unstable solution and misclassification. Second, NRC ignores the fact that data usually lies in a union of multiple affine subspaces, rather than linear subspaces in practical applications. To address the above issues, this paper presents an affine non-negative collaborative representation (ANCR) model for pattern classification. To be more specific, ANCR imposes a regularization term on the coding vector. Moreover, ANCR introduces an affine constraint to better represent the data from affine subspaces. The experimental results on several benchmarking datasets demonstrate the merits of the proposed ANCR method. The source code of our ANCR is publicly available at https://github.com/yinhefeng/ANCR.