PARAMETER OPTIMIZATION METHOD, NON-TRANSITORY RECORDING MEDIUM, FEATURE AMOUNT EXTRACTION METHOD, AND PARAMETER OPTIMIZATION DEVICE

1 . A parameter optimization method comprising:

extracting a feature vector using input data;

acquiring a classification result of the feature vector and a class representative vector of every class serving as a classification target; and

optimizing a parameter used in the extracting based on a classification error obtained using correct answer data and the classification result and a distance error between the class representative vectors such that areas of features of the classes in a feature space do not overlap each other.

2 . The parameter optimization method according to claim 1 , wherein

in the optimizing, a position of the class representative vector of every class in the feature space is determined and then the classification error is optimized using a gradient method, so that the parameter is optimized.

3 . The parameter optimization method according to claim 1 , wherein

in the optimizing, the distance error between the class representative vectors is applied to the classification error and optimization is performed using a gradient method, so that the parameter is optimized.

4 . A non-transitory recording medium configured to record a computer program for causing a computer to execute the parameter optimization method according to claim 1 .

5 . A feature extraction method comprising:

acquiring target data to be classified; and

extracting a feature from the target data, wherein

in the extracting, optimization is performed such that distances between a plurality of classes serving as classification destinations in a feature space are uniform, and the feature is mapped to an area of any of the plurality of classes in the feature space.

6 . A parameter optimization apparatus comprising:

a feature extractor configured to extract a feature vector using input data;

a classificater configured to acquire a classification result of the feature vector and a class representative vector of every class serving as a classification target; and

an optimizer configured to optimize a parameter used in the feature extractor based on a classification error obtained using correct answer data and the classification result and a distance error between the class representative vectors such that areas of features of the classes in a feature space do not overlap each other.

7 . A parameter optimization method comprising:

extracting a feature vector using input data;

acquiring a classification result of the feature vector and a class representative vector of every class serving as a classification target; and

optimizing a parameter used in the extracting based on a classification error obtained using correct answer data and the classification result and a distance error between the class representative vectors, wherein

in the optimizing, a position of the class representative vector of every class in the feature space is determined and then the classification error is optimized using a gradient method, so that the parameter is optimized.

8 . A parameter optimization method comprising:

extracting a feature vector using input data;

acquiring a classification result of the feature vector and a class representative vector of every class serving as a classification target; and

optimizing a parameter used in the extracting based on a classification error obtained using correct answer data and the classification result and a distance error between the class representative vectors, wherein

in the optimizing, the distance error between the class representative vectors is applied to the classification error and optimization is performed using a gradient method, so that the parameter is optimized.