NDLI: High dimensional regression using the sparse matrix transform (SMT)

Content Provider	IEEE Xplore Digital Library
Author	Cao, Guangzhi Guo, Yandong Bouman, Charles A.
Copyright Year	2010
Description	Author affiliation: School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA (Guo, Yandong; Bouman, Charles A.) \|\| GE Healthcare Technologies, 3000 N. Grandview Blvd, W-1180, Waukesha, WI 53188, USA (Cao, Guangzhi)
Abstract	Regression from high dimensional observation vectors is particularly difficult when training data is limited. More specifically, if the number of sample vectors n is less than dimension of the sample vectors p, then accurate regression is difficult to perform without prior knowledge of the data covariance. In this paper, we propose a novel approach to high dimensional regression for application when n ≪ p. The approach works by first decorrelating the high dimensional observation vector using the sparse matrix transform (SMT) estimate of the data covariance. Then the decorrelated observations are used in a regularized regression procedure such as Lasso or shrinkage. Numerical results demonstrate that the proposed regression approach can significantly improve the prediction accuracy, especially when n is small and the signal to be predicted lies in the subspace of the observations corresponding to the small eigenvalues.
Starting Page	1870
Ending Page	1873
File Size	242009
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495359
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse matrices Surface-mount technology Decorrelation Eigenvalues and eigenfunctions Training data Least squares methods Accuracy Computational efficiency Medical services Contracts sparse matrix transform High dimensional regression covariance estimation
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
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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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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