Non-negative Sparse Semantic Coding for Text Categorization

In text categorization, the dimensionality reduction methods, such as latent semantic indexing and non-negative matrix factorization, commonly yield the dense representation that is not consistent with our common knowledge. On the other hand, the popular sparse coding methods are time-consuming and their dictionaries might contain negative entries, which is difficulty to interpret the semantic meaning of text. This paper proposes a novel Non-negative Sparse Semantic Coding (NSSC) approach for text reprentation. NSSC provides an efficient algorithm to construct a set of non-negative basis vectors that span a low dimensional semantic sub-space, where each document obtains a non-negative sparse representation corresponding to these basis vectors. Extensive experimental results have shown that the proposed approach achieves a good performance and presents more interpretability with respect to these semantic concepts.