Data compression and learning in time sequences analysis

Details Data compression and learning in time sequences analysis Data compression and learning in time sequences analysis

2002-07-12

arxiv.org/abs/cond-mat/0207321v1

Physica D 180, 92 (2003)

Physics

Condensed Matter

Statistical Mechanics

15 pages, 6 figures, submitted for publication

Scientific paper

10.1016/S0167-2789(03)00047-2

Motivated by the problem of the definition of a distance between two sequences of characters, we investigate the so-called learning process of typical sequential data compression schemes. We focus on the problem of how a compression algorithm optimizes its features at the interface between two different sequences $A$ and $B$ while zipping the sequence $A+B$ obtained by simply appending $B$ after $A$. We show the existence of a universal scaling function (the so-called learning function) which rules the way in which the compression algorithm learns a sequence $B$ after having compressed a sequence $A$. In particular it turns out that it exists a crossover length for the sequence $B$, which depends on the relative entropy between $A$ and $B$, below which the compression algorithm does not learn the sequence $B$ (measuring in this way the relative entropy between $A$ and $B$) and above which it starts learning $B$, i.e. optimizing the compression using the specific features of $B$. We check the scaling function on three main classes of systems: Bernoulli schemes, Markovian sequences and the symbolic dynamic generated by a non trivial chaotic system (the Lozi map). As a last application of the method we present the results of a recognition experiment, namely recognize which dynamical systems produced a given time sequence. We finally point out the potentiality of these results for segmentation purposes, i.e. the identification of homogeneous sub-sequences in heterogeneous sequences (with applications in various fields from genetic to time-series analysis).

Affiliated with

Also associated with

No associations

Rating

Data compression and learning in time sequences analysis does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Data compression and learning in time sequences analysis, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Data compression and learning in time sequences analysis will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-710063