6. A computer-implemented method of text classification based on sparse representation employing at least one hardware implemented computer processor, the method comprising: representing in a computer process an input text document as a document feature vector y; accessing in a computer process a category dictionary H of possible examples [h 1 ; h 2 ; . . . ; h n ] of the document feature vector y; and automatically classifying in a computer process the input text document based on a sparse representation text classification approach to solve for y=Hβ and enforcing a sparseness condition β to select examples from the dictionary H to describe the document feature vector y.
6. A computer-implemented method of text classification based on sparse representation employing at least one hardware implemented computer processor, the method comprising: representing in a computer process an input text document as a document feature vector y; accessing in a computer process a category dictionary H of possible examples [h 1 ; h 2 ; . . . ; h n ] of the document feature vector y; and automatically classifying in a computer process the input text document based on a sparse representation text classification approach to solve for y=Hβ and enforcing a sparseness condition β to select examples from the dictionary H to describe the document feature vector y. 7. A method according to claim 6 , wherein β is determined by Approximate Bayesian Compressive Sensing (ABCS).