A near pattern-matching scheme based upon principal component analysis
References (13)
- et al.
Computer-Aided Multivariate Analysis
(1990) - et al.
Efficient string matching: an aid to bibliographic search
Comm. ACM
(1975) - et al.
Matching string patterns in large textual files
- et al.
A fast string searching algorithm
Comm. ACM
(1977) - et al.
On the generalized Karhunen-Loeve expansion
IEEE Trans. Inform. Theory
(1967) Sequential Methods in Pattern Recognition and Machine Learning
(1968)
There are more references available in the full text version of this article.
Cited by (18)
Fast k-nearest neighbors search using modified principal axis search tree
2010, Digital Signal Processing: A Review JournalFast exact k nearest neighbors search using an orthogonal search tree
2010, Pattern RecognitionFast k-nearest-neighbor search based on projection and triangular inequality
2007, Pattern RecognitionPCA-Based algorithm for unsupervised bridge crack detection
2006, Advances in Engineering SoftwareCitation 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].
Association of different prediction methods for determination of the efficiency and selectivity on neuron-based sensors
2006, Biosensors and BioelectronicsAn Improved VQ Codebook Search Algorithm Using Principal Component Analysis
1997, Journal of Visual Communication and Image Representation
Copyright © 1995 Published by Elsevier B.V.