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Adaptive Pair-Weight Vector Projection Classifier for Semi-Supervised Classification
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DOI：10.1109/TCE.2023.3284088.png)
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摘要
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Manifold regularization is a popular technology based on graph theory and has been widely applied to nonparallel hyperplane classifiers for semi-supervised learning. However, these classifiers adopt the fixed similarity matrix that may be sensitive to noises and outliers. Thus, we propose a novel nonparallel hyperplane classifier, named the adaptive pair-weight vector projection (APVP) classifier, for semi-supervised classification tasks. The proposed APVP unifies projection learning and graph construction into a general framework. For projection learning, APVP achieves two projection vectors by solving the pair-wise optimization problems in which we need to simultaneously maximize the between-class scatter of data and minimize both the within-class scatter of data and the adaptive manifold regularization. For graph construction, the adaptive similarity matrix is constructed during the procedure of optimization. Thus, APVP can weaken the effect of noises and outliers by the adaptive similarity matrix and strengthen the discriminant capability by the Fisher criterion. To classify unseen data, we also design a new classification rule. Experimental results show that APVP has good performance in semi-supervised scene.
Keyword:
Manifolds
Support vector machines
Laplace equations
Optimization
Sparse matrices
Semisupervised learning
Classification algorithms
Classification
semi-supervised learning
Fisher criterion
adaptive neighbor graph
vector projection
期刊
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10.9
论文数:
5.3K
被引数:
6.8K
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