Using Low-Rank Approximation In Order To Improve the Efficiency of the Support Vector Machine and Applications

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Esmaeil Beigi Mohsen; CHATRABGOUN OMID

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Paper Information

Journal: JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT) Year:2019 | Volume:8 | Issue:4 Page(s): 98-109

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Title

Using Low-Rank Approximation In Order To Improve the Efficiency of the Support Vector Machine and Applications

Author(s)

Esmaeil Beigi Mohsen | CHATRABGOUN OMID | Issue Writer Certificate

Keywords

Support vector machineQ1

Kernel based SVMQ1

Mercer seriesQ1

Low rank approximationQ1

Abstract

Support vector machine is one of the most powerful tools in the field of supervised machine learning to classify the existed data. In the data that the linear Support vector machine does not have the required efficiency in their classification, using the kernel-based Support vector machine which is based on the use of feature space instead of the original data is considered. As a result of this structure, nonlinear classification can be provided. One of the challenges in this approach is to increase the computational complexity and ultimately increase in the required time for classification. As such, it is not particularly useful for large data sets. This increasing in computational time is mainly due to the appearance of the kernel in solving the quadratic optimization problem, which we will be able to overcome this problem using the presented Low rank approximation in this paper. In this technique, using a truncated Mercer series of the kernel, the quadratic optimization problem in the kernel-based Support vector machine is replaced with a much simpler optimization problem. In the new presented approach, the required vector computations and matrix decompositions will be much faster such that these changes lead to faster resolution of the quadratic optimization problem and increase efficiency. Finally, the results of experiments show that using a low rank kernel-based approximation of Support vector machine, while keeping the classification performance in an acceptable range, the computational time has been significantly reduced.

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APA: Copy

Esmaeil Beigi, Mohsen, & CHATRABGOUN, OMID. (2019). Using Low-Rank Approximation In Order To Improve the Efficiency of the Support Vector Machine and Applications. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), 8(4 ), 98-109. SID. https://sid.ir/paper/245903/en

Vancouver: Copy

Esmaeil Beigi Mohsen, CHATRABGOUN OMID. Using Low-Rank Approximation In Order To Improve the Efficiency of the Support Vector Machine and Applications. JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT)[Internet]. 2019;8(4 ):98-109. Available from: https://sid.ir/paper/245903/en

IEEE: Copy

Mohsen Esmaeil Beigi, and OMID CHATRABGOUN, “Using Low-Rank Approximation In Order To Improve the Efficiency of the Support Vector Machine and Applications,” JOURNAL OF SOFT COMPUTING AND INFORMATION TECHNOLOGY (JSCIT), vol. 8, no. 4 , pp. 98–109, 2019, [Online]. Available: https://sid.ir/paper/245903/en

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