Robust nonparallel support vector machine with privileged information for pattern recognition

Liming Liu

doi:10.1007/s13042-022-01709-1

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Robust nonparallel support vector machine with privileged information for pattern recognition

2022-12-03

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DOI：10.1007/s13042-022-01709-1

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Abstract

En 中文

In the field of pattern recognition, collected data always include some additional information which are usually termed as privileged information. The privileged information is latent information belonging to the training samples, which can be easily ignored. The privileged information can help to build a better classifier for classification. In this paper, we try to construct a robust nonparallel support vector machine (NPSVM) model under the privileged information learning (LUPI) setting, termed as R-NPSVM+. On the one hand, we introduce the privileged information into NPSVM so as to build a model for classification. In the training process, both the privileged information and usual samples are used to train the model, which can enhance the accuracy. On the other hand, due to the epsilon-insensitive loss and hinge loss, NPSVM is sensitive to noise or outliers. Hence, we use two robust loss functions in R-NPSVM+ model, which can further ensure the robustness of the model. In addition, we use the Lagrange multiplier method and the dual coordinate descent (DCD) algorithm to optimize the proposed objective function, respectively. Lastly, to evaluate the performance of R-NPSVM+, we conduct a series of experiments. Experimental results confirm that compared with other classical SVM-type algorithms, our R-NPSVM+ can produce a better performance, especially when the samples are corrupted by noise and outliers.

Keywords:

Pattern recognition

Nonparallel support vector machine

Privileged information

Generalization performance

Anti-noise