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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 11
3340-3345

Multiple empirical kernel learning based on intra-class variance and locality-sensitive hashing

Huang Jinbo
Su Xingwang
Wu Lin
Xu Ruyu
Wang Xiaoming
School of Computer & Software Engineer, Xihua University, Chengdu 610039, China
DOI: 10.19734/j.issn.1001-3695.2022.04.0171

Abstract

MREKLM selects a small number of samples to construct the empirical feature space, but does not take into account the distribution of data when projecting, and takes a long time to select samples. In order to utilize the distribution information of samples, this paper introduced the intra-class dispersion matrix, and proposed an intra-class variance based multiple empirical kernel learning(ICVMEKL), which enabled samples to take into account the intra-class information when projecting. The method enhanced the classification boundary and improved the classification accuracy. Further, in order to reduce the selection time of samples, this paper proposed an improved ICVMEKL(ICVMEKL_I) based on locality-sensitive hashing algorithm by using the border point extraction based on locality-sensitive hashing(BPLSH) to select samples, so that the samples for constructing the empirical kernel no longer needed to be obtained from the candidate set, and the method reduced the training time. Experiments on multiple datasets show that ICVMEKL can effectively improve the accuracy, ICVMEKL_I can significantly reduce the training time, both of which show good generalization performance.

Foundation Support

四川省自然科学基金资助项目(2022NSFSC0533)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.04.0171
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 11
Section: Algorithm Research & Explore
Pages: 3340-3345
Serial Number: 1001-3695(2022)11-022-3340-06

Publish History

[2022-06-20] Accepted Paper
[2022-11-05] Printed Article

Cite This Article

黄金玻, 粟兴旺, 吴琳, 等. 基于类内方差和位置敏感哈希的多经验核学习 [J]. 计算机应用研究, 2022, 39 (11): 3340-3345. (Huang Jinbo, Su Xingwang, Wu Lin, et al. Multiple empirical kernel learning based on intra-class variance and locality-sensitive hashing [J]. Application Research of Computers, 2022, 39 (11): 3340-3345. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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