A Collaborative Representation-based Method of Discriminant Locality Preserving Projections

doi:10.3969/j.issn.1006-2475.2023.06.008

Abstract

Abstract: Discriminant locality preserving projections （DLPP） is an effective discriminative feature extraction approach based on manifold learning. It is one of the typical representative algorithms of flow pattern dimensionality reduction. It can use the discriminant information to extract the best discriminant features. However, it fails to exploit the collaborative reconstruction relationship between samples, and thus usually leads to a lower recognition rate. To cope with this problem, a collaborative representation based discriminant locality preserving projections （CRDLPP） is proposed. CRDLPP first calculates the collaborative reconstruction errors of all the samples by collaborative representation mechanism, and then incorporates them as a regularization term into the objective function of DLPP. The optimal projection matrix is finally obtained by solving the new objective optimization problem. In order to verify the good performance of CRDLPP method in image recognition, this paper selects public image databases such as Yale and coil20 for experiments. The results show that the CRDLPP algorithm in this paper has a higher recognition rate than other classical data dimensionality reduction algorithms in image recognition.

Key words: discriminant locality preserving projections, collaborative representation, collaborative reconstruction error, image recognition

CLC Number:

TP391.41

LI Shi-qiu. A Collaborative Representation-based Method of Discriminant Locality Preserving Projections[J]. Computer and Modernization, 2023, 0(06): 43-47.

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