30. The method defined in claim 29 , wherein said estimating step also uses at least one masking matrix that is a sum of an identity matrix and at least one summand masking matrix multiplied by a weight, said summand masking matrix setting certain components of a third orthogonal expansion of a subvector of a third feature vector equal to zero, as said masking matrix is multiplied to said third orthogonal expansion, to compute a representation of a probability distribution of a label of said first feature vector, said third orthgonal expansion comprising components of said subvector of said third feature vector and a plurality of products of said components of said subvector of said third feature vector.
29. A method for processing feature vectors, said method comprising: (g) an expanding step of expanding a subvector of a first feature vector into a first orthogonal expansion that comprises components of said subvector of said first feature vector and a plurality of products of said components of said subvector of said first feature vector, and (h) an estimating step of using i. at least one orthogonal expansion of a subvector of said first feature vector produced by said expanding step; and ii. at least one expansion correlation matrix that is a weighted sum of outer products, each being an outer product of a weighted sum of a vector with components all equal to 1 and a label of a second feature vector and a second orthogonal expansion of a subvector of said second feature vector, said second orthogonal expansion comprising components of said subvector of said second feature vector and a plurality of products of said components of said subvector of said second feature vector; to compute a representation of a probability distribution of a label of said first feature vector.