Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data

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Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data (2001)

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by Robert T. Olszewski , Christos Faloutsos , David Banks Dot

Venue:	In Time-Series Data, PhD dissertation, Carnagie Mellon University
Citations:	32 - 0 self

BibTeX

@TECHREPORT{Olszewski01generalizedfeature,
    author = {Robert T. Olszewski and Christos Faloutsos and David Banks Dot},
    title = {Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data},
    institution = {In Time-Series Data, PhD dissertation, Carnagie Mellon University},
    year = {2001}
}

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Abstract

Pattern recognition encompasses two fundamental tasks: description and classification. Given an object to analyze, a pattern recognition system first generates a description of it (i.e., the pattern) and then classifies the object based on that description (i.e., the recognition). Two general approaches for implementing pattern recognition systems, statistical and structural, employ different techniques for description and classification. Statistical approaches to pattern recognition use decision-theoretic concepts to discriminate among objects belonging to different groups based upon their quantitative features. Structural approaches to pattern recognition use syntactic grammars to discriminate among objects belonging to different groups based upon the arrangement of their morphological (i.e., shape-based or structural) features. Hybrid approaches to pattern recognition combine aspects of both statistical and structural pattern recognition. Structural pattern recognition systems are difficult to apply to new domains because implementation of both the description and classification tasks requires domain knowledge. Knowledge acquisition

Keyphrases

structural pattern recognition time-series data feature extraction pattern recognition system different group decision-theoretic concept quantitative feature pattern recognition fundamental task hybrid approach classification task recognition combine aspect general approach new domain syntactic grammar employ different technique statistical approach structural approach knowledge acquisition structural pattern recognition system

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