Derivation of an exact solution on a principal component analysis for a set of root visual patterns
Identifieur interne : 001D65 ( Main/Exploration ); précédent : 001D64; suivant : 001D66Derivation of an exact solution on a principal component analysis for a set of root visual patterns
Auteurs : Taizo Iijima ; Yasuhiro AokiSource :
- Systems and Computers in Japan [ 0882-1666 ] ; 2000-07.
English descriptors
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Abstract
An integral equation must be solved in order to analyze principal components for a set of general visual patterns. But in the practical case, we usually depend on numerical analysis by processing the data with a computer system. For the purpose of analyzing sets of similar patterns we propose a typical set called a “set of root visual patterns” because it enables us to derive an exact solution for the above equation. In this paper, we succeed in formulating the distribution of eigenvalues for general similar pattern sets from the exact solution. Further, we make sure that the formula is well applied to some practical cases, and it appears that these results will bring useful insights for improving the performance of pattern discrimination methods. © 2000 Scripta Technica, Syst Comp Jpn, 31(8): 49–58, 2000
Url:
DOI: 10.1002/1520-684X(200007)31:8<49::AID-SCJ6>3.0.CO;2-O
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">An integral equation must be solved in order to analyze principal components for a set of general visual patterns. But in the practical case, we usually depend on numerical analysis by processing the data with a computer system. For the purpose of analyzing sets of similar patterns we propose a typical set called a “set of root visual patterns” because it enables us to derive an exact solution for the above equation. In this paper, we succeed in formulating the distribution of eigenvalues for general similar pattern sets from the exact solution. Further, we make sure that the formula is well applied to some practical cases, and it appears that these results will bring useful insights for improving the performance of pattern discrimination methods. © 2000 Scripta Technica, Syst Comp Jpn, 31(8): 49–58, 2000</div>
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