DocumentCode :
674877
Title :
Kronecker sum decompositions of space-time data
Author :
Greenewald, Kristjan ; Tsiligkaridis, Theodoros ; Hero, Alfred O.
Author_Institution :
Univ. of Michigan, Ann Arbor, MI, USA
fYear :
2013
fDate :
15-18 Dec. 2013
Firstpage :
65
Lastpage :
68
Abstract :
In this paper we consider the use of the space vs. time Kronecker product decomposition in the estimation of covariance matrices for spatio-temporal data. This decomposition imposes lower dimensional structure on the estimated covariance matrix, thus reducing the number of samples required for estimation. To allow a smooth tradeoff between the reduction in the number of parameters (to reduce estimation variance) and the accuracy of the covariance approximation (affecting estimation bias), we introduce a diagonally loaded modification of the sum-of-kronecker products representation in [1].We derive an asymptotic Cramér-Rao bound (CRB) on the minimum attainable mean squared predictor coefficient estimation error for unbiased estimators of Kronecker structured covariance matrices. We illustrate the accuracy of the diagonally loaded Kronecker sum decomposition by applying it to the prediction of human activity video.
Keywords :
covariance matrices; matrix decomposition; mean square error methods; video signal processing; Kronecker structured covariance matrices; Kronecker sum decompositions; asymptotic Cramer-Rao bound; covariance approximation; estimation variance; human activity video; mean squared predictor coefficient estimation error; space-time data; spatiotemporal data; Accuracy; Approximation methods; Covariance matrices; Estimation; Legged locomotion; Load modeling; Standards;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013 IEEE 5th International Workshop on
Conference_Location :
St. Martin
Print_ISBN :
978-1-4673-3144-9
Type :
conf
DOI :
10.1109/CAMSAP.2013.6714008
Filename :
6714008
Link To Document :
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