DocumentCode
3383704
Title
The distributed, partial, and conditional Karhunen-Loeve transforms
Author
Gastpar, Michael ; Dragotti, Pier-Luigi ; Vetterli, Martin
Author_Institution
Dept. of EECS, California Univ., Berkeley, CA, USA
fYear
2003
fDate
25-27 March 2003
Firstpage
283
Lastpage
292
Abstract
The Karhunen-Loeve transform (KLT) is a key element of many signal processing tasks, including approximation, compression, and classification. Many recent applications involve distributed signal processing where it is not generally possible to apply the KLT to the signal; the KLT must be approximated in a distributed fashion. Investigations were carried out on the distributed approximations to the KLT. First, explicit solutions to special cases were presented including a partial KLT, a conditional KLT, and the combination of these two special cases. These results were used to derive an algorithm that finds the best distributed approximation to the KLT. Applications of the results from sensor networks and distributed databases were discussed.
Keywords
Karhunen-Loeve transforms; approximation theory; data compression; signal processing; Karhunen-Loeve transform; conditional KLT; distributed KLT; distributed databases; distributed signal processing; partial KLT; sensor networks; side information; signal approximation; signal classification; signal compression; signal processing tasks; source subset; Approximation algorithms; Cameras; Data compression; Decoding; Distributed databases; Educational institutions; Karhunen-Loeve transforms; Layout; Signal processing; Signal processing algorithms;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Compression Conference, 2003. Proceedings. DCC 2003
ISSN
1068-0314
Print_ISBN
0-7695-1896-6
Type
conf
DOI
10.1109/DCC.2003.1194019
Filename
1194019
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