NDLI: Class-imbalance learning based discriminant analysis

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyuan Jing Chao Lan Min Li Yongfang Yao Zhang, D. Jingyu Yang
Copyright Year	2011
Description	Author affiliation: College of Computer Science, Nanjing University of Science and Technology, 210094, China (Jingyu Yang) \|\| State Key Laboratory of Software Engineering, Wuhan University, 430079, China (Xiaoyuan Jing) \|\| College of Automation, Nanjing University of Posts and Telecommunications, 210046, China (Chao Lan; Min Li; Yongfang Yao) \|\| Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong (Zhang, D.)
Abstract	Feature extraction is an important research topic in the field of pattern recognition. The class-specific idea tends to recast a traditional multi-class feature extraction and recognition task into several binary class problems, and therefore inevitably class imbalance problem, where the minority class is the specific class, and the majority class consists of all the other classes. However, discriminative information from binary class problems is usually limited, and imbalanced data may have negative effect on the recognition performance. For solving these problems, in this paper, we propose two novel approaches to learn discriminant features from imbalanced data, named class-balanced discrimination (CBD) and orthogonal CBD (OCBD). For a specific class, we select a reduced counterpart class whose data are nearest to the data of specific class, and further divide them into smaller subsets, each of which has the same size as the specific class, to achieve balance. Then, each subset is combined with the minority class, and linear discriminant analysis (LDA) is performed on them to extract discriminative vectors. To further remove redundant information, we impose orthogonal constraint on the extracted discriminant vectors among correlated classes. Experimental results on three public image databases demonstrate that the proposed approaches outperform several related image feature extraction and recognition methods.
Starting Page	545
Ending Page	549
File Size	787789
Page Count	5
File Format	PDF
ISBN	9781457701221
e-ISBN	9781457701214
DOI	10.1109/ACPR.2011.6166659
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Handwriting recognition Correlation Class-imbalance learning Discriminant analysis Databases Image feature extraction and recognition Feature extraction Educational institutions Vectors Class balanced discrimination (CBD) Orthogonal CBD (OCBD)
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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	Mr. Siddhartha Mukherjee siddhartha@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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	ndl-support@iitkgp.ac.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Libarray of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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