Group Low-Rank Representation-Based Discriminant Linear Regression

In this paper, a novel least square regression method, named group low-rank representation-based discriminant linear regression (GLRRDLR), is proposed for multi-class classification. Unlike the conventional linear regression methods, the proposed method aims to learn a more discriminative projection...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2020-03, Vol.30 (3), p.760-770
Main Authors: Zhan, Shanhua, Wu, Jigang, Han, Na, Wen, Jie, Fang, Xiaozhao
Format: Article
Language:English
Subjects:
Constraint modelling
Face recognition
group low-rank constraint
Kernel
Least square regression
Linear regression
low-rank representation
Measurement uncertainty
multi-class classification
Noise measurement
Projection
Regression
Regression analysis
Representations
Training
Transforms
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Summary:In this paper, a novel least square regression method, named group low-rank representation-based discriminant linear regression (GLRRDLR), is proposed for multi-class classification. Unlike the conventional linear regression methods, the proposed method aims to learn a more discriminative projection. Specially, two main techniques are adopted to improve the discriminability of the projection. The first approach is to make the transformed samples locate in their own subspace by introducing a group low-rank constraint to the model, such that the distance between samples from the same class can be decreased greatly. The second approach is to simultaneously learn a discriminative target matrix for regression. The extensive experimental results show that the proposed method performs much better than the state-of-the-art methods, which proves the effectiveness of the above two approaches in improving the discriminability of the projection.
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2019.2897072