1. An information processing apparatus, comprising: combination generating means for obtaining a first feature quantity of N dimensions, N being an integer of at least two, from first information prepared for execution of learning and using said first feature quantity to generate at least two first feature quantity combinations having corresponding dimensions that fail to exceed the N dimensions of said first feature quantity; and learning processing executing means for computing a correlation coefficient between said first feature quantity combinations and a learning model feature quantity matching each dimension of said first feature quantity combinations and, by use of said first correlation coefficient, classifying said first information to execute learning processing for classifying predetermined second information, wherein said combination generating means is configured to: obtain a matrix comprising elements corresponding to the N dimensions of the first feature quantity, the elements being output by Gabor filters associated with corresponding ones of the N dimensions and being arranged within the matrix according to filter directions and frequencies associated with the Gabor filters; and generate said first feature quantity combinations by combining elements within corresponding continuous areas of the matrix, the continuous areas being associated with ranges of the filter directions and frequencies of the Gabor filters.