1. A sparse representation-based text classification engine employing at least one hardware implemented computer processor and comprising: a text input module that receives an input text document and represents it as a document feature vector y; a category dictionary H of possible examples [h 1 ; h 2 ; . . . ; h n ] of the document feature vector y; and a text classifier that receives the document feature vector y and accesses the category dictionary H to automatically classify 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.
1. A sparse representation-based text classification engine employing at least one hardware implemented computer processor and comprising: a text input module that receives an input text document and represents it as a document feature vector y; a category dictionary H of possible examples [h 1 ; h 2 ; . . . ; h n ] of the document feature vector y; and a text classifier that receives the document feature vector y and accesses the category dictionary H to automatically classify 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. 4. A text classification engine according to claim 1 , wherein the text classifier 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 .