Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data. (12th August 2018)

• Record Type: Journal Article
• Authors: Deng, Wan-Yu; Liu, Dan; Dong, Ying-Ying
• Other Names: Zak Arkadiusz Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2018(2018)
• Publication Date: 2018-08-12
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2018/1583969
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.