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m-SNE: Multiview Stochastic Neighbor Embedding
Xie, Bo1; Mu, Yang2; Tao, Dacheng3; Huang, Kaiqi4; Huang,Kaiqi
AbstractDimension reduction has been widely used in real-world applications such as image retrieval and document classification. In many scenarios, different features (or multiview data) can be obtained, and how to duly utilize them is a challenge. It is not appropriate for the conventional concatenating strategy to arrange features of different views into a long vector. That is because each view has its specific statistical property and physical interpretation. Even worse, the performance of the concatenating strategy will deteriorate if some views are corrupted by noise. In this paper, we propose a multiview stochastic neighbor embedding (m-SNE) that systematically integrates heterogeneous features into a unified representation for subsequent processing based on a probabilistic framework. Compared with conventional strategies, our approach can automatically learn a combination coefficient for each view adapted to its contribution to the data embedding. This combination coefficient plays an important role in utilizing the complementary information in multiview data. Also, our algorithm for learning the combination coefficient converges at a rate of O(1/k(2)), which is the optimal rate for smooth problems. Experiments on synthetic and real data sets suggest the effectiveness and robustness of m-SNE for data visualization, image retrieval, object categorization, and scene recognition.
KeywordDimension Reduction Image Retrieval Multiview Learning Stochastic Neighbor Embedding
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000293708200017
Citation statistics
Cited Times:69[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorHuang,Kaiqi
Affiliation1.Chinese Univ Hong Kong, Dept Elect Engn, Shatin, Hong Kong, Peoples R China
2.Univ Massachusetts, Dept Comp Sci, Boston, MA 02125 USA
3.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Fac Engn & Informat Technol, Sydney, NSW 2700, Australia
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Xie, Bo,Mu, Yang,Tao, Dacheng,et al. m-SNE: Multiview Stochastic Neighbor Embedding[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2011,41(4):1088-1096.
APA Xie, Bo,Mu, Yang,Tao, Dacheng,Huang, Kaiqi,&Huang,Kaiqi.(2011).m-SNE: Multiview Stochastic Neighbor Embedding.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,41(4),1088-1096.
MLA Xie, Bo,et al."m-SNE: Multiview Stochastic Neighbor Embedding".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 41.4(2011):1088-1096.
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