Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
661-667,680

Probabilistic Riemannian quantification method with log-Euclidean metric learning

Zhang Xiaocheng1,2,3
Tang Fengzhen1,2
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2. Institutes for Robotics & Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
DOI: 10.19734/j.issn.1001-3695.2021.09.0353

Abstract

In many machine learning applications, the data may be symmetric positive definite(SPD) matrices which are not living in Euclidean space. This paper presented a new probabilistic Riemannian space quantization method based on log-Euclidean metric learning. The proposed method extended the Euclidean probabilistic learning vector quantization(PLVQ) method to deal with SPD matrices by treating them as points on the Riemannian manifold of SPD matrices equipped with log-Euclidean metric, through utilizing a parameterized distance function from log-Euclidean metric learning. On the BCI IV 2a dataset, the proposed method outperformed Euclidean PLVQ by 20% in terms of recognition accuracy. The proposed method also performs better than the first winner of BCI competition IV on this data set. It obtains comparable classification accuracy to PLVQ using affine invariant Riemannian metric, but requires much less computing time, i. e. only needs 1% of the training time, while 10% of the test time. The proposed method also obtains superior performance on the BCI III IIIa and ETH-80 datasets, showing its effectiveness and efficiency.

Foundation Support

国家自然科学基金资助项目(61803369)
中国科学院大学生创新实践训练计划资助项目(E01Z010601)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.09.0353
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Algorithm Research & Explore
Pages: 661-667,680
Serial Number: 1001-3695(2022)03-003-0661-07

Publish History

[2021-11-29] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

张晓铖, 唐凤珍. 基于对数欧氏度量学习的概率黎曼空间量化方法 [J]. 计算机应用研究, 2022, 39 (3): 661-667,680. (Zhang Xiaocheng, Tang Fengzhen. Probabilistic Riemannian quantification method with log-Euclidean metric learning [J]. Application Research of Computers, 2022, 39 (3): 661-667,680. )

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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