Nonlinear set to set pattern recognition
First Claim
1. A method of classifying a data set of related unlabeled patterns, the method comprising:
- encoding a collection of data sets of patterns onto a parameter space, each data set being encoded to at least one point on the parameter space, each data set further being allocated to a labeled array of data sets, each labeled array being designated to a class;
defining a mapping operator for each class that maps the data sets of the labeled array onto the parameter space in satisfaction of a similarity criterion based on the encoded points of that labeled array on the parameter space;
encoding the data set of related unlabeled patterns to at least one point on the parameter space;
generating a similarity measurement for each class based on the encoded point of the data set of related unlabeled patterns by mapping the data set of related unlabeled patterns on the parameter space using the mapping operator for the class;
labeling the data set of related unlabeled patterns as a member of a class, if the similarity measurement associated with the mapping operator of the class satisfies a classification criterion.
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Accused Products
Abstract
Variations in the states of patterns can be exploited for their discriminatory information and should not be discarded as noise. A pattern recognition system compares a data set of unlabeled patterns having variations of state in a set-by-set comparison with labeled arrays of individual data sets of multiple patterns also having variations of state. The individual data sets are each mapped to a point on a parameter space, and the points of each labeled array define a subset of the parameter space. If the point associated with the data set of unlabeled patterns satisfies a similarity criterion on the parameter space subset of a labeled array, the data set of unlabeled patterns is assigned to the class attributed to that labeled array.
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Citations
20 Claims
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1. A method of classifying a data set of related unlabeled patterns, the method comprising:
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encoding a collection of data sets of patterns onto a parameter space, each data set being encoded to at least one point on the parameter space, each data set further being allocated to a labeled array of data sets, each labeled array being designated to a class; defining a mapping operator for each class that maps the data sets of the labeled array onto the parameter space in satisfaction of a similarity criterion based on the encoded points of that labeled array on the parameter space; encoding the data set of related unlabeled patterns to at least one point on the parameter space; generating a similarity measurement for each class based on the encoded point of the data set of related unlabeled patterns by mapping the data set of related unlabeled patterns on the parameter space using the mapping operator for the class; labeling the data set of related unlabeled patterns as a member of a class, if the similarity measurement associated with the mapping operator of the class satisfies a classification criterion. - View Dependent Claims (2, 3, 4, 5, 6, 7)
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8. One or more computer readable storage media storing computer executable instructions for executing a computer process classifying a data set of related unlabeled patterns on a computing system, the computer process comprising:
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encoding a collection of data sets of patterns onto a parameter space, each data set being encoded to at least one point on the parameter space, each data set further being allocated to a labeled array of data sets, each labeled array being designated to a class; defining a mapping operator for each class that maps the data sets of the labeled array onto the parameter space in satisfaction of a similarity criterion based on the encoded points of that labeled array on the parameter space; encoding the data set of related unlabeled patterns to at least one point on the parameter space; generating a similarity measurement for each class based on the encoded point of the data set of related unlabeled patterns by mapping the data set of related unlabeled patterns on the parameter space using the mapping operator for the class; labeling the data set of related unlabeled patterns as a member of a class, if the similarity measurement associated with the mapping operator of the labeled array satisfies a classification criterion. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A method of classifying a data set of related unlabeled patterns, the method comprising:
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encoding a collection of data sets of patterns to points on a nonlinear parameter space, wherein data sets allocated to a labeled array are grouped in a region of the nonlinear parameter space, each labeled array being designated to a class; defining a mapping operator for each class that maps the data sets of the labeled array onto the nonlinear parameter space in satisfaction of a similarity criterion based on the encoded points of that labeled array on the nonlinear parameter space; generating a similarity measurement for each class based on the data set of related unlabeled patterns by mapping the data set of related unlabeled patterns on the nonlinear parameter space using the mapping operator for the class; labeling the data set of related unlabeled patterns as a member of a class, if the similarity measurement associated with the mapping operator of the class satisfies a classification criterion. - View Dependent Claims (16, 17, 18, 19, 20)
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Specification