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Multi-view support vector machines with sub-view learning
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AI
DOI：10.1007/s00500-023-07884-9.png)
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Abstract
En 中文
Multi-view learning improves the performance of existing learning tasks by using complementary information between multiple feature sets. In the latest research, multi-view learning model using privileged information is proposed; specific models are PSVM-2V and MCPK. In these models, views complement each other by acting as privileged information policies; however, a single view contains privileged information that can guide the classifier, and the existing framework does not consider it. In order to use this information to correct multi-view support vector machine classifier, we propose a framework for generating a series of small-scale views based on information hidden in a single view, which extends the original multi-view parallel structure to a hierarchical structure with sub-view mechanism. In this paper, two sub-view learning structures SL-PSVM-2V and SL-MCPK are constructed. The two new models fully exploit the data features in the view. Similarly, they follow the principles of consistency and complementarity. We use the standard quadratic programming solver to solve the new model. In 55 groups of classification experiments, the proposed model improves the classification accuracy by about 1.91% on the original basis. SL-MCPK ranks 1.3846 on average in the accuracy experiment, indicating that they have good classification ability. In addition, the computational time statistics and noise data set experiments are carried out to prove the effectiveness of the proposed method from multiple perspectives.
Keywords:
Multi-view learning
Support vector machines
Sub-views
Privileged information