A near pattern-matching scheme based upon principal component analysis

doi:10.1016/0167-8655(94)00109-G

Pattern Recognition Letters

Volume 16, Issue 4, April 1995, Pages 339-345

https://doi.org/10.1016/0167-8655(94)00109-G Get rights and content

Abstract

In this paper, we present an efficient heuristic near pattern-matching scheme. Based upon an important multivariate analysis technique in statistics, called the principal components analysis, we develop algorithms to generate a set of new identifying keys for a given set of patterns to reduce the number of comparisons during the near-matching process. After some preprocessing work, the near-matching operation takes O(n log m) time in the worst case, where m is the number of identifying segments extracted from the patterns to be searched in a text file of length n.

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Citation Excerpt :
This reduction is accomplished by transforming the original set of variables to a new set of variables that are uncorrelated and ordered by their significance, so that the first few variables retain most of the variation present in all of the original data. PCA has many applications in image understanding and pattern recognition that includes pattern matching [13,14], neural networks [11,15], speech analysis [16], visual learning [17,18], and active vision [19]. In feature recognition, PCA has been extensively used to identify face features [20].
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View full text

A near pattern-matching scheme based upon principal component analysis

Abstract

Computer-Aided Multivariate Analysis

Efficient string matching: an aid to bibliographic search

Comm. ACM

Matching string patterns in large textual files

A fast string searching algorithm

Comm. ACM

On the generalized Karhunen-Loeve expansion

IEEE Trans. Inform. Theory

Sequential Methods in Pattern Recognition and Machine Learning