[1]尹宝才,张超辉,胡永利,等.基于图嵌入的自适应多视降维方法[J].智能系统学报,2021,16(5):963-970.[doi:10.11992/tis.202105021]
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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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]
基于图嵌入的自适应多视降维方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
16
期数:
2021年第5期
页码:
963-970
栏目:
吴文俊人工智能科技进步奖一等奖
出版日期:
2021-09-05
- Title:
- An adaptive multi-view dimensionality reduction method based on graph embedding
- Keywords:
- dimensionality reduction; multi-view data; graph embedding; adaptive learning; high-dimensional data; similarity measure; unsupervised learning; representation learning
- 分类号:
- TP18
- 摘要:
- 随着监控摄像头的普及和数据采集技术的快速发展，多视数据呈现出规模大、维度高和多源异构的特点，使得数据存储空间大、传输慢、算法复杂度高，造成“有数据、难利用”的困境。到目前为止，国内外在多视降维方面的研究还比较少。针对这一问题，本文提出一种基于图嵌入的自适应多视降维方法。该方法在考虑视角内降维后数据重构原始高维数据的基础上，提出自适应学习相似矩阵来探索不同视角之间降维后数据的关联关系，学习各视数据的正交投影矩阵实现多视降维任务。本文在多个数据集上对降维后的多视数据进行了聚类/识别实验验证，实验结果表明基于图嵌入的自适应多视降维方法优于其他降维方法。
- 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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备注/Memo
收稿日期:2021-05-13。
基金项目:国家自然科学基金项目（U19B2039，61906011）；北京市自然科学基金项目（4204086）
作者简介:尹宝才，教授，博士生导师，多媒体与智能软件技术北京市重点实验室主任，北京人工智能研究院院长，中国计算机学会人工智能与模式识别专业委员会委员，ACM 北京分会副主席。主要研究方向为多媒体技术、跨媒体智能、视频编码。主持国家973项目、国家自然科学基金重大项目、国家自然科学基金重点项目、北京市自然科学基金重点项目等10余项。发表学术论文60余篇;张超辉，硕士研究生，主要研究方向为机器学习、数据挖掘;胡永利，教授，博士生导师，主要研究方向为模式识别、计算机视觉、跨媒体智能和智能交通。北京市高等学校创新团队?交通大数据处理学术创新团队。主持国家自然科学基金联合基金重点项目、面上项目等10余项。发表学术论文30余篇.
通讯作者:胡永利.E-mail:huyongli@bjut.edu.cn
更新日期/Last Update:
1900-01-01