Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2015

Abstract

Feature learning plays a central role in pattern recognition. In recent years, many representation-based feature learning methods have been proposed and have achieved great success in many applications. However, these methods perform feature learning and subsequent classification in two separate steps, which may not be optimal for recognition tasks. In this paper, we present a supervised low-rank-based approach for learning discriminative features. By integrating latent low-rank representation (LatLRR) with a ridge regression-based classifier, our approach combines feature learning with classification, so that the regulated classification error is minimized. In this way, the extracted features are more discriminative for the recognition tasks. Our approach benefits from a recent discovery on the closed-form solutions to noiseless LatLRR. When there is noise, a robust Principal Component Analysis (PCA)-based denoising step can be added as preprocessing. When the scale of a problem is large, we utilize a fast randomized algorithm to speed up the computation of robust PCA. Extensive experimental results demonstrate the effectiveness and robustness of our method.

Keywords

Feature learning, low-rank representation (LRR), recognition, robust principal component analysis(PCA

Discipline

Artificial Intelligence and Robotics

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

IEEE Transactions on Neural Networks and Learning Systems

Volume

27

Issue

5

First Page

1080

Last Page

1093

ISSN

2162-237X

Identifier

10.1109/TNNLS.2015.2436951

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TNNLS.2015.2436951

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