Spatial representation for efficient sequence classification

Pavel P. Kuksa, Vladimir Pavlovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

We present a general, simple feature representation of sequences that allows efficient inexact matching, comparison and classification of sequential data. This approach, recently introduced for the problem of biological sequence classification, exploits a novel multiscale representation of strings. The new representation leads to discovery of very efficient algorithms for string comparison, independent of the alphabet size. We show that these algorithms can be generalized to handle a wide gamut of sequence classification problems in diverse domains such as the music and text sequence classification. The presented algorithms offer low computational cost and highly scalable implementations across different application domains. The new method demonstrates order-of-magnitude running time improvements over existing state-of-the-art approaches while matching or exceeding their predictive accuracy.

Original languageEnglish (US)
Title of host publicationProceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Pages3320-3323
Number of pages4
DOIs
StatePublished - 2010
Event2010 20th International Conference on Pattern Recognition, ICPR 2010 - Istanbul, Turkey
Duration: Aug 23 2010Aug 26 2010

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2010 20th International Conference on Pattern Recognition, ICPR 2010
Country/TerritoryTurkey
CityIstanbul
Period8/23/108/26/10

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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