Tensor-Based Multi-view Feature Selection with Applications to Brain Diseases

Bokai Cao, Lifang He, Xiangnan Kong, Philip S. Yu, Zhifeng Hao, Ann B. Ragin

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

47 Scopus citations


In the era of big data, we can easily access information from multiple views which may be obtained from different sources or feature subsets. Generally, different views provide complementary information for learning tasks. Thus, multi-view learning can facilitate the learning process and is prevalent in a wide range of application domains. For example, in medical science, measurements from a series of medical examinations are documented for each subject, including clinical, imaging, immunologic, serologic and cognitive measures which are obtained from multiple sources. Specifically, for brain diagnosis, we can have different quantitative analysis which can be seen as different feature subsets of a subject. It is desirable to combine all these features in an effective way for disease diagnosis. However, some measurements from less relevant medical examinations can introduce irrelevant information which can even be exaggerated after view combinations. Feature selection should therefore be incorporated in the process of multi-view learning. In this paper, we explore tensor product to bring different views together in a joint space, and present a dual method of tensor-based multi-view feature selection DUAL-TMFS based on the idea of support vector machine recursive feature elimination. Experiments conducted on datasets derived from neurological disorder demonstrate the features selected by our proposed method yield better classification performance and are relevant to disease diagnosis.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
EditorsRavi Kumar, Hannu Toivonen, Jian Pei, Joshua Zhexue Huang, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781479943029
StatePublished - Jan 1 2014
Event14th IEEE International Conference on Data Mining, ICDM 2014 - Shenzhen, China
Duration: Dec 14 2014Dec 17 2014

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other14th IEEE International Conference on Data Mining, ICDM 2014


  • brain diseases
  • feature selection
  • multi-view learning
  • tensor

ASJC Scopus subject areas

  • General Engineering


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