16. A correspondence learning apparatus according to claim 5 , further comprising: a information update module that updates the covariance matrices C xx , C yy , C xy and C yx in accordance with a following equation (105) and solves the eigenvalue problem of the equation (3) to obtain the transformation to derive the latent variables when a n th combination of a new first feature x(n) and a new second feature y(n) occurs in the presence of n−1 combinations of the first features x(1), . . . , x(i), . . . , x(n−1) and the second feature y(1), . . . , y(i), . . . , y(n−1), where n>=2, 1 represents a decay rate, x ˜ (n) and y ˜ (n) are given in a following equation (106), m x (n) and m y (n) are given in a following equation (107): C xx ⁡ ( n ) = n - 1 - l n ⁢ C xx ⁡ ( n - 1 ) + 1 + l n ⁢ x ~ ⁡ ( n ) ⁢ x ~ T ⁡ ( n ) C yy ⁡ ( n ) = n - 1 - l n ⁢ C yy ⁡ ( n - 1 ) + 1 + l n ⁢ y ~ ⁡ ( n ) ⁢ y ~ T ⁡ ( n ) C xy ⁡ ( n ) = n - 1 - l n ⁢ C xy ⁡ ( n - 1 ) + 1 + l n ⁢ x ~ ⁡ ( n ) ⁢ y ~ T ⁡ ( n ) C yx ⁡ ( n ) = C xy T ⁡ ( n ) } ( 105 ) x ~ ⁡ ( n ) = x ⁡ ( n ) - m x ⁡ ( n ) y ~ ⁡ ( n ) = y ⁡ ( n ) - m y ⁡ ( n ) } ( 106 ) m x ⁡ ( n ) = n - 1 - l n ⁢ m x ⁡ ( n - 1 ) + 1 + l n ⁢ x ⁡ ( n ) m y ⁡ ( n ) = n - 1 - l n ⁢ m y ⁡ ( n - 1 ) + 1 + l n ⁢ y ⁡ ( n ) } . ( 107 )
16. A correspondence learning apparatus according to claim 5 , further comprising: a information update module that updates the covariance matrices C xx , C yy , C xy and C yx in accordance with a following equation (105) and solves the eigenvalue problem of the equation (3) to obtain the transformation to derive the latent variables when a n th combination of a new first feature x(n) and a new second feature y(n) occurs in the presence of n−1 combinations of the first features x(1), . . . , x(i), . . . , x(n−1) and the second feature y(1), . . . , y(i), . . . , y(n−1), where n>=2, 1 represents a decay rate, x ˜ (n) and y ˜ (n) are given in a following equation (106), m x (n) and m y (n) are given in a following equation (107): C xx ⁡ ( n ) = n - 1 - l n ⁢ C xx ⁡ ( n - 1 ) + 1 + l n ⁢ x ~ ⁡ ( n ) ⁢ x ~ T ⁡ ( n ) C yy ⁡ ( n ) = n - 1 - l n ⁢ C yy ⁡ ( n - 1 ) + 1 + l n ⁢ y ~ ⁡ ( n ) ⁢ y ~ T ⁡ ( n ) C xy ⁡ ( n ) = n - 1 - l n ⁢ C xy ⁡ ( n - 1 ) + 1 + l n ⁢ x ~ ⁡ ( n ) ⁢ y ~ T ⁡ ( n ) C yx ⁡ ( n ) = C xy T ⁡ ( n ) } ( 105 ) x ~ ⁡ ( n ) = x ⁡ ( n ) - m x ⁡ ( n ) y ~ ⁡ ( n ) = y ⁡ ( n ) - m y ⁡ ( n ) } ( 106 ) m x ⁡ ( n ) = n - 1 - l n ⁢ m x ⁡ ( n - 1 ) + 1 + l n ⁢ x ⁡ ( n ) m y ⁡ ( n ) = n - 1 - l n ⁢ m y ⁡ ( n - 1 ) + 1 + l n ⁢ y ⁡ ( n ) } . ( 107 ) 17. A correspondence learning apparatus according to claim 16 , wherein the information update module updates the covariance matrices associated with the second feature in accordance with a following equation (108) when the order of the second feature increases with an occurrence of the new first feature x(n) and the new second feature y(n), where m y (n) is given in a following equation (109): C yy ⁡ ( n ) = n - 1 - l n ⁢ ( C yy ⁡ ( n - 1 ) 0 0 0 ) + 1 + l n ⁢ y ~ ⁡ ( n ) ⁢ y ~ T ⁡ ( n ) C xy ⁡ ( n ) = n - 1 - l n ⁢ ( C xy ⁡ ( n - 1 ) ⁢ ⁢ 0 ) + 1 + l n ⁢ x ~ ⁡ ( n ) ⁢ y ~ T ⁡ ( n ) } ( 108 ) m y ⁡ ( n ) = n - 1 - l n ⁢ ( m y ⁡ ( n - 1 ) 0 ) + 1 + l n ⁢ y ⁡ ( n ) . ( 109 )