NDLI: Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm

Content Provider	Springer Nature Link
Author	Li, Yuanqing Guan, Cuntai
Copyright Year	2007
Abstract	The focus of this paper is on joint feature re-extraction and classification in cases when the training data set is small. An iterative semi-supervised support vector machine (SVM) algorithm is proposed, where each iteration consists both feature re-extraction and classification, and the feature re-extraction is based on the classification results from the previous iteration. Feature extraction is first discussed in the framework of Rayleigh coefficient maximization. The effectiveness of common spatial pattern (CSP) feature, which is commonly used in Electroencephalogram (EEG) data analysis and EEG-based brain computer interfaces (BCIs), can be explained by Rayleigh coefficient maximization. Two other features are also defined using the Rayleigh coefficient. These features are effective for discriminating two classes with different means or different variances. If we extract features based on Rayleigh coefficient maximization, a large training data set with labels is required in general; otherwise, the extracted features are not reliable. Thus we present an iterative semi-supervised SVM algorithm embedded with feature re-extraction. This iterative algorithm can be used to extract these three features reliably and perform classification simultaneously in cases where the training data set is small. Each iteration is composed of two main steps: (i) the training data set is updated/augmented using unlabeled test data with their predicted labels; features are re-extracted based on the augmented training data set. (ii) The re-extracted features are classified by a standard SVM. Regarding parameter setting and model selection of our algorithm, we also propose a semi-supervised learning-based method using the Rayleigh coefficient, in which both training data and test data are used. This method is suitable when cross-validation model selection may not work for small training data set. Finally, the results of data analysis are presented to demonstrate the validity of our approach.
Starting Page	33
Ending Page	53
Page Count	21
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	71
Issue Number	1
e-ISSN	15730565
Language	English
Publisher	Springer US
Publisher Date	2007-11-17
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Supporter vector machine (SVM) Semi-supervised learning Rayleigh coefficient Feature extraction Model selection Language Translation and Linguistics Simulation and Modeling Computing Methodologies Automation and Robotics Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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	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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library 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

Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm

Feature selection via sensitivity analysis of SVM probabilistic outputs

Combined SVM-Based Feature Selection and Classification

Semi-Supervised Learning on Riemannian Manifolds

Multicategory Proximal Support Vector Machine Classifiers

An aggregate and iterative disaggregate algorithm with proven optimality in machine learning

Large margin classification with indefinite similarities

Wavelet-based feature extraction for DNA microarray classification

Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics

Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm

Similar Documents

Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm

Feature selection via sensitivity analysis of SVM probabilistic outputs

Combined SVM-Based Feature Selection and Classification

Semi-Supervised Learning on Riemannian Manifolds

Multicategory Proximal Support Vector Machine Classifiers

An aggregate and iterative disaggregate algorithm with proven optimality in machine learning

Large margin classification with indefinite similarities

Wavelet-based feature extraction for DNA microarray classification

Multi-Relational Learning, Text Mining, and Semi-Supervised Learning for Functional Genomics

Joint feature re-extraction and classification using an iterative semi-supervised support vector machine algorithm