NDLI: Joint multitask feature learning and classifier design

Content Provider	IEEE Xplore Digital Library
Author	Gutta, S. Qi Cheng
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Oklahoma State Univ., Stillwater, OK, USA (Gutta, S.; Qi Cheng)
Abstract	The problem of classification arises in many realworld applications. Often classification of more than two classes is broken down into a group of binary classification problems using the one-versus-rest or pairwise approaches. For each binary classification problem, feature selection and classifier design are usually conducted separately. In this paper, we propose a new multitask learning approach in which feature selection and classifier design for all the binary classification tasks are carried out simultaneously. We consider probabilistic nonlinear kernel classifiers for binary classification. For each binary classifier, we give weights to the features within the kernels. We assume that the matrix consisting of all the feature weights for all the tasks has a sparse component and a low rank component. The sparse component determines the features that are relevant to each classifier, and the low rank component determines the common feature subspace that is relevant to all the classifiers. Experimental results on synthetic data demonstrate that the proposed approach achieves higher classification accuracy compared to the conventional classifiers. The proposed method accurately determines the relevant features that are important to each binary classifier.
Sponsorship	IEEE Inf. Theory Soc.
Starting Page	1
Ending Page	5
File Size	570751
Page Count	5
File Format	PDF
ISBN	9781467352376
e-ISBN	9781467352390
e-ISBN	9781467352383
DOI	10.1109/CISS.2013.6552296
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-03-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Classification Vectors sparsity Sparse matrices Matrix decomposition multitask learning Kernel Joints Optimization feature selection
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	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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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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