Multi-View Clustering Via Mixed Embedding Approximation

Danyang Wu, Feiping Nie, Rong Wang, Xuelong Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

Abstract

This paper tackles multi-view clustering via proposing a novel mixed embedding approximation (MEA) method. Formally, we aim to learn a uniform orthogonal embedding based on the orthogonal pre-embeddings of each view. At first, we hope that the uniform embedding can reconstruct the affinity graph of each view. To improve the representation of learnt embedding, we perform an embedding approximation on Grassmann manifold which is famous on subspace analysis. To perform the difference of views, a hidden weights learning module is provided. Moreover, we propose an iterative algorithm to solve the proposed MEA method and provide rigorously convergence analysis. Extensive experiments demonstrate the superiorities of the proposed method.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3977-3981
Number of pages5
ISBN (Electronic)9781509066315
DOIs
StatePublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
Duration: 4 May 20208 May 2020

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2020-May
ISSN (Print)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20

Keywords

  • Grassmann Manifold
  • Hidden Weights Learning
  • Multi-view Clustering

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