Patent Document ID: 8423485
Application ID: 12733561

Base Claim:
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.

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Claim 6:
6. A correspondence learning apparatus according to claim 1 , wherein the information deriving module includes a probability density distribution setting module that sets a probability density distribution indicating a probability of occurrence of the arbitrary first feature from the latent variable for each of the latent variables, and a symbol occurrence probability calculating module that calculates a symbol occurrence probability being a probability of occurrence of the symbol from the latent variable for each of symbols different from each other among all of the second data, and wherein the information storage stores the probability density distribution and the symbol occurrence probability as the information for each of the latent variables.