Embedding Regularizer Learning for Multi-View Semi-Supervised Classification
| Autor: | Yannan Zheng, Zheng Wang, Chia-Wen Lin, Tiesong Zhao, Aiping Huang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Optimization problem
business.industry Computer science Deep learning Machine learning computer.software_genre Computer Graphics and Computer-Aided Design Statistical classification ComputingMethodologies_PATTERNRECOGNITION Robustness (computer science) Norm (mathematics) Outlier Embedding Artificial intelligence business computer Software Sparse matrix |
| Zdroj: | IEEE Transactions on Image Processing. 30:6997-7011 |
| ISSN: | 1941-0042 1057-7149 |
| DOI: | 10.1109/tip.2021.3101917 |
| Popis: | Classification remains challenging when confronted with the existence of multi-view data with limited labels. In this paper, we propose an embedding regularizer learning scheme for multi-view semi-supervised classification (ERL-MVSC). The proposed framework integrates diversity, sparsity and consensus to dexterously manipulate multi-view data with limited labels. To encourage diversity, ERL-MVSC recasts a linear regression model to derive view-specific embedding regularizers and automatically determines their weights. This is able to tactfully incorporate complementary information of different views. To ensure sparsity, ERL-MVSC imposes $\ell _{2,1}$ -norm on a fused embedding regularizer to exploit the sparse local structure of samples, thereby conveying valuable classification information and enhancing the robustness against noise/outliers. To enhance consensus, ERL-MVSC learns a shared predicted label matrix, which serves as the comment target of multi-view classification. With these techniques, we formulate ERL-MVSC as a joint optimization problem of an embedding regularizer and a predicted label matrix, which can be solved by a coordinate descent method. Extensive experimental results on real-world datasets demonstrate the effectiveness and superiority of the proposed algorithm. |
| Databáze: | OpenAIRE |
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