[1]YIN Baocai,ZHANG Chaohui,HU Yongli,et al.An adaptive multi-view dimensionality reduction method based on graph embedding[J].CAAI Transactions on Intelligent Systems,2021,16(5):963-970.[doi:10.11992/tis.202105021]
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An adaptive multi-view dimensionality reduction method based on graph embedding

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 5 Page number: 963-970 Column: 吴文俊人工智能科技进步奖一等奖 Public date: 2021-09-05
Title:
An adaptive multi-view dimensionality reduction method based on graph embedding
Author(s):
YIN Baocai1; 2;  ZHANG Chaohui1;  HU Yongli1; 2;  SUN Yanfeng1; 2;  WANG Boyue1; 2
1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
2. Beijing Artificial Intelligence Institute, Beijing 100124, China
Keywords:
dimensionality reduction; multi-view data; graph embedding; adaptive learning; high-dimensional data; similarity measure; unsupervised learning; representation learning
CLC:
TP18
DOI:
10.11992/tis.202105021
Abstract:
With the popularity of surveillance cameras and the rapid development of data acquisition technology, multi-view data shows the traits of large scale, high dimension and multi-source heterogeneity, which cause large data storage, low data transmission speed and high algorithm complexity, resulting in a predicament that “there are plenty of data that are hard to use”. Up to now, few domestic and foreign researches have been done on multi-view dimensionality reduction. In order to solve this problem, this paper proposes an adaptive multi-view dimensionality reduction method based on graph embedding. In consideration of the reconstructed high-dimensional data after the view-angle dimensionality reduction, this method puts forward an adaptive similarity matrix to explore the correlation between dimension-reduced data from different perspectives and learn the orthogonal projection matrix of each perspective to achieve the multi-view dimensionality reduction task. In this paper, a clustering/recognition verification experiment is performed on the dimension-reduced multi-view data from multiple data sets. The experimental results present that the proposed method is better than other dimensionality reduction methods.
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