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Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data
Joint Authors
Deng, Wan-Yu
Liu, Dan
Dong, Ying-Ying
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-12
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Due to missing values, incomplete dataset is ubiquitous in multimodal scene.
Complete data is a prerequisite of the most existing multimodality data fusion methods.
For incomplete multimodal high-dimensional data, we propose a feature selection and classification method.
Our method mainly focuses on extracting the most relevant features from the high-dimensional features and then improving the classification accuracy.
The experimental results show that our method produces considerably better performance on incomplete multimodal data such as ADNI dataset and Office dataset, compared to the case of complete data.
American Psychological Association (APA)
Deng, Wan-Yu& Liu, Dan& Dong, Ying-Ying. 2018. Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1205692
Modern Language Association (MLA)
Deng, Wan-Yu…[et al.]. Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data. Mathematical Problems in Engineering No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1205692
American Medical Association (AMA)
Deng, Wan-Yu& Liu, Dan& Dong, Ying-Ying. Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1205692
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1205692