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
Link To Document :
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