7. A system according to claim 6 , further comprising: an analyzer to evaluate each category-conditional likelihood of occurrence p(x n |c) as a latent conditionally independent distribution model expressed by the equation: p ⁡ ( x n | c ) = ∑ k = 1 K ⁢ p k ⁡ ( c ) ⁢ ∏ d = 1 D ⁢ p k ⁡ ( x nd | c ) where k is an index to an independent component, 0≦k ≦K; d denotes a dimension index, 1≦d ≦D; x nd denotes a value of the d th dimension of feature x n ; p k (c) denotes a category-conditional probability that a feature is generated from the k th independent component; p k (x n |c) denotes a category-and-latent conditional likelihood that a feature, x n , has x nd as a value of its d th dimension.
7. A system according to claim 6 , further comprising: an analyzer to evaluate each category-conditional likelihood of occurrence p(x n |c) as a latent conditionally independent distribution model expressed by the equation: p ⁡ ( x n | c ) = ∑ k = 1 K ⁢ p k ⁡ ( c ) ⁢ ∏ d = 1 D ⁢ p k ⁡ ( x nd | c ) where k is an index to an independent component, 0≦k ≦K; d denotes a dimension index, 1≦d ≦D; x nd denotes a value of the d th dimension of feature x n ; p k (c) denotes a category-conditional probability that a feature is generated from the k th independent component; p k (x n |c) denotes a category-and-latent conditional likelihood that a feature, x n , has x nd as a value of its d th dimension. 9. A system according to claim 7 , wherein for the dimensions comprise discrete finite valued attributes and each category-and-latent conditional likelihood is modeled as a multinomial probability mass function for each such discrete dimension.