Generalized and Discriminative Collaborative Representation for Multiclass Classification
| Autor: | Luoqing Li, Yap-Peng Tan, Cuiming Zou, Yulong Wang, Yuan Yan Tang, Hong Chen |
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| Rok vydání: | 2022 |
| Předmět: |
Training set
Computer science business.industry Machine learning computer.software_genre Regularization (mathematics) Computer Science Applications Human-Computer Interaction Multiclass classification ComputingMethodologies_PATTERNRECOGNITION Discriminative model Control and Systems Engineering Leverage (statistics) Artificial intelligence Electrical and Electronic Engineering business computer Software Information Systems |
| Zdroj: | IEEE Transactions on Cybernetics. 52:2675-2686 |
| ISSN: | 2168-2275 2168-2267 |
| DOI: | 10.1109/tcyb.2020.3021712 |
| Popis: | This article presents a generalized collaborative representation-based classification (GCRC) framework, which includes many existing representation-based classification (RC) methods, such as collaborative RC (CRC) and sparse RC (SRC) as special cases. This article also advances the GCRC theory by exploring theoretical conditions on the general regularization matrix. A key drawback of CRC and SRC is that they fail to use the label information of training data and are essentially unsupervised in computing the representation vector. This largely compromises the discriminative ability of the learned representation vector and impedes the classification performance. Guided by the GCRC theory, we propose a novel RC method referred to as discriminative RC (DRC). The proposed DRC method has the following three desirable properties: 1) discriminability: DRC can leverage the label information of training data and is supervised in both representation and classification, thus improving the discriminative ability of the representation vector; 2) efficiency: it has a closed-form solution and is efficient in computing the representation vector and performing classification; and 3) theory: it also has theoretical guarantees for classification. Experimental results on benchmark databases demonstrate both the efficacy and efficiency of DRC for multiclass classification. |
| Databáze: | OpenAIRE |
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