A data processing method, includes: mapping each of a plurality of data, for which classes the data belong to are known, to one point on an N-dimensional feature space using at least two feature amounts; dividing a set of points corresponding to the plurality of data mapped on the feature space into a plurality of N-dimensional simplexes having each point as an apex; classifying a set of points that constitute a hyperplane of each simplex obtained by the division into a subset including points that belong to the same class as elements; and reducing the elements of the subsets for each of the classified subsets. The dividing includes dividing the set of points into the plurality of simplexes so a hypersphere circumscribed on each simplex does not include a point that constitutes another simplex.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data processing method executed by a processor, comprising: mapping each of a plurality of data, for which classes the data belong to are known, to one point on an N-dimensional (N is an integer of not less than 2 or infinity) feature space using at least two feature amounts; dividing a set of points corresponding to the plurality of data mapped on the feature space into a plurality of N-dimensional simplexes having each point as an apex; classifying a set of points that constitute a hyperplane of each simplex obtained by the division into a subset including points that belong to the same class as elements; and reducing the elements of the subsets for each of the classified subsets, wherein the dividing comprises dividing the set of points into the plurality of simplexes so a hypersphere circumscribed on each simplex does not include a point that constitutes another simplex. 2. The method according to claim 1, wherein the reducing comprises reducing, of the elements constituting each of the classified subsets, two elements having a minimum Euclidean distance on the feature space into one new element. 3. The method according to claim 2, wherein the reducing further comprises: setting a class of the new element obtained by the reduction to the same class as a class to which the two elements of reduction targets belong; and repeating the dividing, the classifying, and the reducing for a plurality of data including the new element obtained by the reducing. 4. The method according to claim 1, further comprising generating a discriminator configured to discriminate a class to which arbitrary data belongs by performing machine learning of the reduced data. 5. The method according to claim 4, wherein the generating comprises performing the machine learning using a support vector machine. 6. The method according to claim 1, wherein the mapping comprises mapping, as the plurality of data, a plurality of support vectors that are data selected by machine learning using a support vector machine from a plurality of training data for which the classes the data belong to are known. 7. A data processing apparatus comprising: a database configured to store a plurality of data for which the classes the data belong to are known; a mapping unit configured to map each of the plurality of data to one point on an N-dimensional (N is an integer of not less than 2 or infinity) feature space using at least two feature amounts; a data division unit configured to divide a set of points corresponding to the plurality of data mapped on the feature space into a plurality of N-dimensional simplexes having each point as an apex; a classification unit configured to classify a set of points that constitute a hyperplane of each simplex obtained by the division into a subset including points that belong to the same class as elements; and a data reduction unit configured to reduce the elements of the subsets for each of the classified subsets, wherein the data division unit is further configured to divide the set of points into the plurality of simplexes so a hypersphere circumscribed on each simplex does not include a point that constitutes another simplex. 8. A non-transitory computer-readable storage medium storing a computer program, the computer program, executed by at least processor of an apparatus, comprising: an instruction to cause the apparatus to map each of a plurality of data, for which the classes that the data belong to are known, to one point on an N-dimensional (N is an integer of not less than 2 or infinity) feature space using at least two feature amounts; an instruction to cause the apparatus to divide a set of points corresponding to the plurality of data mapped on the feature space into a plurality of N-dimensional simplexes having each point as an apex; an instruction to cause the apparatus to classify a set of points that constitute a hyperplane of each simplex obtained by the division into a subset including points that belong to the same class as elements; and an instruction to cause the apparatus to reduce the elements of the subsets for each of the classified subsets, wherein the instruction to cause the apparatus to divide further causes the apparatus to divide the set of points into the plurality of simplexes so a hypersphere circumscribed on each simplex does not include a point that constitutes another simplex.