4. A computer-implemented method for classification, comprising: using an input/output device receiving a text represented by a plurality of features for classification; and using a processor device configured to perform: selecting a subset from the plurality of features by subspace sampling using a probability distribution over a plurality of features from a corpus of training texts, the probability distribution having a probability assigned to each of the plurality of features that is proportional to a square of Euclidean norms of a plurality of rows of a plurality of left singular vectors of a matrix of the plurality of features representing the corpus of training texts; classifying the text using the subset of the plurality of features; and outputting the classification of the text classified using the subset of the plurality of features selected by subspace sampling.
4. A computer-implemented method for classification, comprising: using an input/output device receiving a text represented by a plurality of features for classification; and using a processor device configured to perform: selecting a subset from the plurality of features by subspace sampling using a probability distribution over a plurality of features from a corpus of training texts, the probability distribution having a probability assigned to each of the plurality of features that is proportional to a square of Euclidean norms of a plurality of rows of a plurality of left singular vectors of a matrix of the plurality of features representing the corpus of training texts; classifying the text using the subset of the plurality of features; and outputting the classification of the text classified using the subset of the plurality of features selected by subspace sampling. 10. The method of claim 4 wherein selecting the subset of the plurality of features from the plurality of features by subspace sampling further comprises determining an optimal vector representing the subset of the plurality of features that characterize a classification function using a kernel matrix defined over the subset of the plurality of features.