초록 ▼

A feature extraction system for statistically analyzing a set of samples of feature vectors to calculate a feature being an index for a pattern identification, which is capable of identifying confusing data with a high robustness. In this system, a storage section stores a feature vector inputted th

A feature extraction system for statistically analyzing a set of samples of feature vectors to calculate a feature being an index for a pattern identification, which is capable of identifying confusing data with a high robustness. In this system, a storage section stores a feature vector inputted through an input section and a neighborhood vector selection section selects a specific feature vector from the feature vectors existing in the storage section. The specific feature is a neighborhood vector close in distance to the feature vector stored in the storage section. Further, the system is equipped with a feature vector space production section for outputting a partial vector space. The partial vector space is made to maximize the local scattering property of the feature vector when the feature vector is orthogonally projected to that space.

대표청구항 ▼

[ What is claimed is:] [1.] A feature extraction system for statistically analyzing a set of samples of feature vectors to calculate an amount indicative of a feature which plays an important role when pattern-identification is performed, said system comprising:feature vector input means for inputti

[ What is claimed is:] [1.] A feature extraction system for statistically analyzing a set of samples of feature vectors to calculate an amount indicative of a feature which plays an important role when pattern-identification is performed, said system comprising:feature vector input means for inputting a feature vector;storage means for storing said feature vector inputted through said input means;neighborhood vector selection means for selecting a neighborhood vector, being a feature vector close to said feature vector inputted and stored in said storage means, from feature vectors existing in said storage means; andfeature vector space production means for outputting a partial vector space in which feature vectors locally most disperse when said feature vectors are orthogonally projected thereinto,wherein, when said feature vectors stored in said storage means are taken to be V.sub.i (1.ltoreq.i.ltoreq.N) and the dimension of these feature vectors is taken as n, said neighbor vector selection means selects M (.ltoreq.N) feature vectors close to these feature vectors through the use of a distance d(x, y), where x and y represent feature vectors, determined according to the following equation (1): ]EQU16 ] and said feature vector space production means obtains eigenvectors by solving an eigenvalue problem of a local covariance matrix given from the following equation (2): ]EQU17 ] where V.sub.j.sup.(i) denotes a neighborhood vector to V.sub.i and n(i) depicts the total number of possible V.sub.j.sup.(i) which varies at every i, moreover, said feature vector space production means selects m (.ltoreq.N, n) eigenvectors from the obtained eigenvectors in the order of decreasing corresponding eigenvalue to output them as bases of a partial vector space which maximizes a local scattering property.