NDLI: Minimum Information Quantity Partition based fast semantic feature subset selection

Content Provider	IEEE Xplore Digital Library
Author	Donglin Cao Yanping, L.V. Dazhen Lin
Copyright Year	2015
Description	Author affiliation: Cognitive Sci. Dept., Xiamen Univ., Xiamen, China (Donglin Cao; Yanping, L.V.; Dazhen Lin)
Abstract	Processing of web text clustering data usually results in more than ten thousand features. The traditional dimensionality reduction methods and optimal feature subset selection methods increase the time complexity. To reduce the time complexity, we propose a Minimum Information Quantity Partition (MIQP) method. First, MIQP selects a useful feature subset by determining the best partition according to the diminishing trend of feature weight curve. Second, to remove the feature independent assumption and compute the semantic relation between selected features, Latent Semantic Indexing (LSI) is used to eliminate noisy data and extend the missed semantic of each sample. This approach reduces the time complexity from $O(mn^{3})$ to $O(mn^{2}).$ The experimental results show that the performance of MIQP is close to the best clustering results of selecting top k features, and the speed of MIQP is much faster than clustering with all features in our experiment data.
Starting Page	692
Ending Page	699
File Size	424579
Page Count	8
File Format	PDF
e-ISBN	9781467376822
DOI	10.1109/FSKD.2015.7382026
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Error analysis Semantics Fast Semantic Feature Subset Selection Minimum Information Quantity Partition Latent Semantic Indexing Cognitive science Noise measurement Time complexity Intelligent systems Indexing
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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