4. A correspondence learning apparatus according to claim 1 , wherein a first variate represents a variate obtained by projecting the first feature on a canonical space, a first transformation represents a transformation to obtain the first variate from the first feature, a second variate represents a variate obtained by projecting the second feature on the canonical space, a second transformation represents a transformation to obtain the second variate from the second feature, wherein the canonical correlation analysis module obtains the first and second transformations that maximize a correlation between first variates and second variates by the canonical correlation analysis and a center of a normal distribution defined by one combination of the first and second feature in the canonical space, and wherein the analysis result storage stores the obtained first and second transformation, and the center of the normal distribution obtained for each of the combinations of the first and second features.
1. A correspondence learning apparatus that learns a correspondence between real-world information and symbols corresponding to the real-world information, the apparatus comprising: a first feature storage that stores a plurality of first features respectively extracted from first data as a feature of the first data that indicates the real-world information; a second feature storage that stores a plurality of second features respectively extracted from second data as a feature of the second data that corresponds to the first data and indicates at least one symbol corresponding to the real-world information; a canonical correlation analysis module that performs a canonical correlation analysis based on a plurality of combinations of the first and second features so as to obtain a transformation to derive latent variables based on at least one of the first and second features, the latent variables respectively indicating an abstract concept that connects the real-world information with the symbol corresponding to the real-world information; an analysis result storage that stores the transformation obtained by the canonical correlation analysis module and the latent variables obtained using the transformation for each of the combinations of the first and second features; an information deriving module that derives information required to obtain a probability of occurrence of an arbitrary first feature from the latent variable and a probability of occurrence of an arbitrary second feature from the latent variable for each of the latent variables; and an information storage that stores information derived by the information deriving module.