Title :
Data Compression and Linear Modeling
Author :
Beheshti, Soosan
Author_Institution :
Ryerson Univ., Toronto
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;
Conference_Titel :
Data Compression Conference, 2008. DCC 2008
Conference_Location :
Snowbird, UT
Print_ISBN :
978-0-7695-3121-2
DOI :
10.1109/DCC.2008.66
