DocumentCode
3226398
Title
Data Compression and Linear Modeling
Author
Beheshti, Soosan
Author_Institution
Ryerson Univ., Toronto
fYear
2008
fDate
25-27 March 2008
Firstpage
507
Lastpage
507
Abstract
This paper addresses problem of data compression when partial information on data structure is available and optimum code is known to be among a set of given parametric codes. The goal of the proposed method is to choose the optimum parametric code by using an observed finite length data that is generated by an unknown parameter. We provide a new approach that compares estimates of different order among the given parametric codes and chooses the one with minimum probabilistic worst-case average codelength (ACL).
Keywords
codes; data compression; data structures; average codelength; data compression; data structure; linear modeling; optimum code; parametric codes; Data compression; Data structures; Maximum likelihood estimation; Parameter estimation; Probability; Random processes; Random variables; Samarium; Statistics; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Compression Conference, 2008. DCC 2008
Conference_Location
Snowbird, UT
ISSN
1068-0314
Print_ISBN
978-0-7695-3121-2
Type
conf
DOI
10.1109/DCC.2008.66
Filename
4483334
Link To Document