Stochastic gradient support vector machine with local structural information for pattern recognition

doi:10.1007/s13042-021-01303-x

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Stochastic gradient support vector machine with local structural information for pattern recognition

2021-04-15

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DOI：10.1007/s13042-021-01303-x

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Abstract

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Structural information is very important for improving the classification performance of classifiers. In order to increase the generalization performance of support vector machine (SVM) directly, several kinds of structured SVMs have been proposed. These algorithms with structural information only simply embed the global structural information or the local within-class information into SVM model. Thus, they sometimes are not suitable for real-world problems. To overcome the drawbacks, we firstly propose a novel SVM with local structural information (LSI-SVM) in this paper. In the LSI-SVM, the K-nearest neighbor (KNN) method is adopted. Applying the KNN method, the farthest neighbors set intra-class and the nearest neighbors set inter-class of the overall samples are obtained. It is more reasonable to maximize the margin between the nearest neighbors and the farthest neighbors. Both the global and local data structures are added into the optimization problem, making the LSI-SVM can fully utilize the underlying structural information and yield better performance. Furthermore, for nonlinear classification, the reproducing kernel Hilbert space theory is introduced and the kernel-based LSI-SVM is generated. What's more, in order to accelerate the training speed of LSI-SVM, a stochastic gradient LSI-SVM (LSI-SVM +) is constructed using the stochastic gradient descent (SGD) solver. Lastly, experimental results on regular-scale datasets, steel surface defects datasets, ORL face dataset and large-scale datasets demonstrate that both LSI-SVM and LSI-SVM + outperform other state-of-the-art algorithms on accuracy. In the meanwhile, our LSI-SVM + has high efficiency.

Keywords:

Pattern recognition

Support vector machine

Structural information of data

Stochastic gradient descent

Generalization performance

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