Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data

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...

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Main Authors:	Wan-Yu Deng, Dan Liu, Ying-Ying Dong
Format:	Article
Language:	English
Published:	Hindawi Limited 2018-01-01
Series:	Mathematical Problems in Engineering
Online Access:	http://dx.doi.org/10.1155/2018/1583969

id	doaj-f74e89ad4c4b48e78d0e68b956bf7f94
record_format	Article
spelling	doaj-f74e89ad4c4b48e78d0e68b956bf7f942020-11-24T22:21:49ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/15839691583969Feature Selection and Classification for High-Dimensional Incomplete Multimodal DataWan-Yu Deng0Dan Liu1Ying-Ying Dong2School of Computer, Xi’an University of Post & Telecommunications, Shaanxi, ChinaSchool of Computer, Xi’an University of Post & Telecommunications, Shaanxi, ChinaSchool of Computer, Xi’an University of Post & Telecommunications, Shaanxi, ChinaDue 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.http://dx.doi.org/10.1155/2018/1583969
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Wan-Yu Deng Dan Liu Ying-Ying Dong
spellingShingle	Wan-Yu Deng Dan Liu Ying-Ying Dong Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data Mathematical Problems in Engineering
author_facet	Wan-Yu Deng Dan Liu Ying-Ying Dong
author_sort	Wan-Yu Deng
title	Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data
title_short	Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data
title_full	Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data
title_fullStr	Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data
title_full_unstemmed	Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data
title_sort	feature selection and classification for high-dimensional incomplete multimodal data
publisher	Hindawi Limited
series	Mathematical Problems in Engineering
issn	1024-123X 1563-5147
publishDate	2018-01-01
description	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.
url	http://dx.doi.org/10.1155/2018/1583969
work_keys_str_mv	AT wanyudeng featureselectionandclassificationforhighdimensionalincompletemultimodaldata AT danliu featureselectionandclassificationforhighdimensionalincompletemultimodaldata AT yingyingdong featureselectionandclassificationforhighdimensionalincompletemultimodaldata
_version_	1725769690944372736

Feature Selection and Classification for High-Dimensional Incomplete Multimodal Data

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