12. A correspondence learning apparatus according to claim 10 , wherein y i represents the second feature, w represents the second feature of the second data indicating a single symbol, t i represents the second variate obtained by projecting the second feature y i on the canonical space, and sz i represents the latent variable s i or z i , and wherein the symbol occurrence probability calculating module calculates a probability P(w|sz i ) of a following equation (8) as the symbol occurrence probability for each of symbols different from each other among all of the second data, where, in the equation (8), q represents an order of the second feature w, W y =C yy B, and ξ y C yy −W y W T y : P ⁡ ( w ❘ sz i ) = 1 ( 2 ⁢ π ) q ⁢  ξ y  ⁢ exp ⁡ ( - 1 2 ⁢ ( w - W y ⁢ t i - y _ ) T ⁢ ξ y - 1 ⁡ ( w - W y ⁢ t i - y _ ) ) . ( 8 )
10. A correspondence learning apparatus according to claim 4 , wherein x represents an arbitrary first feature, y represents an arbitrary second feature, z i represents the latent variable, and Φ xy represents a variance of the normal distribution defined one combination of the first and second feature in the canonical space, wherein the information deriving module includes a probability density distribution setting module that obtains the variance Φ xy in accordance with a following equation (102) and sets a probability density distribution P(x|z i ) indicating a probability of occurrence of the arbitrary first feature x from the latent variable z i for each latent variable z i , 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 z i for each of symbols different from each other among all of the second data in accordance with a following equation (103), and wherein the information storage stores the probability density distribution P(x|z i ) and the symbol occurrence probability as the information for each latent variable z i , where, in the equation (103), K represents a normalization constant to normalize a sum of the probabilities, Φx represents a variance of a normal distribution defined by the arbitrary first feature x in the canonical space and is given in a following equation (104), zc represents a center of the normal distribution and is given in a following equation (104): Φ xy = I - ( M x M y ) T ⁢ ( ( I - Λ 2 ) - 1 - ( I - Λ 2 ) - 1 ⁢ Λ - ( I - Λ 2 ) - 1 ⁢ Λ ( I - Λ 2 ) - 1 ) ⁢ ( M x M y ) ( 102 ) P ⁡ ( x ❘ z i ) = K · exp ⁡ ( - 1 8 ⁢ ( zc - z i ) T ⁢ ( Φ x + Φ xy 2 ) - 1 ⁢ ( zc - z i ) ) ( 103 ) Φ x = I - M x ⁢ M x T zc = M x T ⁢ A T ⁡ ( x - m x ) } . ( 104 )