On the uniqueness of positive semidefinite matrix solution under compressed observations

Weiyu Xu; Ao Tang

doi:10.1109/ISIT.2010.5513532

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Conference proceeding

On the uniqueness of positive semidefinite matrix solution under compressed observations

Weiyu Xu and Ao Tang

2010 IEEE International Symposium on Information Theory, pp.1523-1527

06/2010

DOI: 10.1109/ISIT.2010.5513532

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Abstract

In this paper, we investigate the uniqueness of positive semidefinite matrix solution to compressed linear observations. We show that under a necessary and sufficient condition for the linear compressed observations operator, there will be a unique positive semidefinite matrix solution to the compressed linear observations. It is further shown, through concentration of measure phenomenon and sphere covering arguments, that a randomly generated Gaussian linear compressed observations operator will satisfy this necessary and sufficient condition with overwhelmingly large probability.

Algorithm design and analysis

Covariance matrix

Linear systems

Machine learning algorithms

Polynomials

Signal processing algorithms

Sparse matrices

Statistics

Sufficient conditions

Symmetric matrices

Details

Title: Subtitle: On the uniqueness of positive semidefinite matrix solution under compressed observations
Creators: Weiyu Xu - Cornell University
Ao Tang - Cornell University
Resource Type: Conference proceeding
Publication Details: 2010 IEEE International Symposium on Information Theory, pp.1523-1527
Publisher: IEEE
DOI: 10.1109/ISIT.2010.5513532
ISSN: 2157-8095
eISSN: 2157-8117
Language: English
Date published: 06/2010
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984197326202771

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