Sparse representations for text classification

摘要

A sparse representation method of text classification is described. An input text document is represented as a document feature vector y. A category dictionary H provides possible examples [h1; h2; . . . ; hn] of the document feature vector y. The input text document is classified using a sparse representation text classification algorithm that solves for y=Hbeta where a sparseness condition is enforced on beta to select a small number of examples from the dictionary H to describe the document feature vector y.