A computer implemented method comprising: projecting a set of training vectors in a vector space to a comparison space of dimensionality M defined by a set of M reference vectors using a comparison function to generate a corresponding set of N projected training vectors in the comparison space wherein each projected training vector has dimensionality M; training a linear classifier on the set of projected training vectors to generate a trained linear classifier operative in the comparison space; transforming the trained linear classifier operative in the comparison space into a trained nonlinear classifier operative in the vector space; receiving an input vector of said vector space so as to represent an object; classifying said input vector of said vector space by applying said trained nonlinear classifier; and outputting a classification of said input vector; and wherein the vector space has dimensionality D and the comparison function comprises a vector element comparison for each dimension of the vector space summed over the dimensions of the vector space, and the transforming comprises: for a first dimension of the vector space, determining a one-dimensional function based on (i) the vector element comparison of the comparison function for the first dimension of the vector space and (ii) trained parameters of the trained linear classifier; and repeating the determining for the remaining dimensions of the vector space; wherein the determining comprises: for the first dimension of the vector space, determining the one-dimensional function φ d as φ d x i d = ∑ m = 1 M w m ⋅ f d x i d, r m d where d denotes the first dimension of the vector space, x i denotes an i th training vector of the set of training vectors, r m denotes the m th reference vector of the set of M reference vectors, and f d denotes the vector element comparison of the comparison function for the first dimension of the vector space. projecting a set of training vectors in a vector space to a comparison space of dimensionality M defined by a set of M reference vectors using a comparison function to generate a corresponding set of N projected training vectors in the comparison space wherein each projected training vector has dimensionality M; training a linear classifier on the set of projected training vectors to generate a trained linear classifier operative in the comparison space; transforming the trained linear classifier operative in the comparison space into a trained nonlinear classifier operative in the vector space; receiving an input vector of said vector space so as to represent an object; classifying said input vector of said vector space by applying said trained nonlinear classifier; and outputting a classification of said input vector; and wherein the vector space has dimensionality D and the comparison function comprises a vector element comparison for each dimension of the vector space summed over the dimensions of the vector space, and the transforming comprises: for a first dimension of the vector space, determining a one-dimensional function based on (i) the vector element comparison of the comparison function for the first dimension of the vector space and (ii) trained parameters of the trained linear classifier; and repeating the determining for the remaining dimensions of the vector space; wherein the determining comprises: for the first dimension of the vector space, determining the one-dimensional function φ d as φ d x i d = ∑ m = 1 M w m ⋅ f d x i d, r m d where d denotes the first dimension of the vector space, x i denotes an i th training vector of the set of training vectors, r m denotes the m th reference vector of the set of M reference vectors, and f d denotes the vector element comparison of the comparison function for the first dimension of the vector space. for a first dimension of the vector space, determining a one-dimensional function based on (i) the vector element comparison of the comparison function for the first dimension of the vector space and (ii) trained parameters of the trained linear classifier; and repeating the determining for the remaining dimensions of the vector space; wherein the determining comprises: for the first dimension of the vector space, determining the one-dimensional function φ d as φ d x i d = ∑ m = 1 M w m ⋅ f d x i d, r m d where d denotes the first dimension of the vector space, x i denotes an i th training vector of the set of training vectors, r m denotes the m th reference vector of the set of M reference vectors, and f d denotes the vector element comparison of the comparison function for the first dimension of the vector space.