@inproceedings{8821165e67794777a5422f2f007c47f2,
title = "Tensor-Based Multi-view Feature Selection with Applications to Brain Diseases",
abstract = "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.",
keywords = "brain diseases, feature selection, multi-view learning, tensor",
author = "Bokai Cao and Lifang He and Xiangnan Kong and Yu, {Philip S.} and Zhifeng Hao and Ragin, {Ann B.}",
year = "2014",
month = jan,
day = "1",
doi = "10.1109/ICDM.2014.26",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "January",
pages = "40--49",
editor = "Ravi Kumar and Hannu Toivonen and Jian Pei and {Zhexue Huang}, Joshua and Xindong Wu",
booktitle = "Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014",
address = "United States",
edition = "January",
note = "14th IEEE International Conference on Data Mining, ICDM 2014 ; Conference date: 14-12-2014 Through 17-12-2014",
}