11. A computer program product for text classification implemented in a tangible computer readable storage medium, the product comprising: program code for representing an input text document as a document feature vector y; program code for accessing a category dictionary H of possible examples [h 1 ; h 2 ; . . . ; h n ] of the document feature vector y; and program code for automatically classifying 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.
11. A computer program product for text classification implemented in a tangible computer readable storage medium, the product comprising: program code for representing an input text document as a document feature vector y; program code for accessing a category dictionary H of possible examples [h 1 ; h 2 ; . . . ; h n ] of the document feature vector y; and program code for automatically classifying 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. 14. A product according to claim 11 , wherein the text classification approach uses a maximum l 2 classification rule that assigns the document feature vector y to a best class i* within the category dictionary H which has largest l 2 norm based on a selector δi (β)ε m to compute an 1 2 norm for β for a class i as ∥δi (β)∥ 2 .