Adaptive structural co-regularization for unsupervised multi-view feature selection

Tsung Yu Hsieh, Yiwei Sun, Suhang Wang, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the advent of big data, there is an urgent need for methods and tools for integrative analyses of multi-modal or multi-view data. Of particular interest are unsupervised methods for parsimonious selection of non-redundant, complementary, and information-rich features from multi-view data. We introduce Adaptive Structural Co-Regularization Algorithm (ASCRA) for unsupervised multi-view feature selection. ASCRA jointly optimizes the embeddings of the different views so as to maximize their agreement with a consensus embedding which aims to simultaneously recover the latent cluster structure in the multi-view data while accounting for correlations between views. ASCRA uses the consensus embedding to guide efficient selection of features that preserve the latent cluster structure of the multi-view data. We establish ASCRA's convergence properties and analyze its computational complexity. The results of our experiments using several real-world and synthetic data sets suggest that ASCRA outperforms or is competitive with state-of-the-art unsupervised multi-view feature selection methods.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019
EditorsYunjun Gao, Ralf Moller, Xindong Wu, Ramamohanarao Kotagiri
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages87-96
Number of pages10
ISBN (Electronic)9781728146065
DOIs
StatePublished - Nov 2019
Event10th IEEE International Conference on Big Knowledge, ICBK 2019 - Beijing, China
Duration: Nov 10 2019Nov 11 2019

Publication series

NameProceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019

Conference

Conference10th IEEE International Conference on Big Knowledge, ICBK 2019
CountryChina
CityBeijing
Period11/10/1911/11/19

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Decision Sciences (miscellaneous)
  • Information Systems and Management
  • Management Science and Operations Research
  • Safety, Risk, Reliability and Quality

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  • Cite this

    Hsieh, T. Y., Sun, Y., Wang, S., & Honavar, V. (2019). Adaptive structural co-regularization for unsupervised multi-view feature selection. In Y. Gao, R. Moller, X. Wu, & R. Kotagiri (Eds.), Proceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019 (pp. 87-96). [8944744] (Proceedings - 10th IEEE International Conference on Big Knowledge, ICBK 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBK.2019.00020