Document clustering via matrix representation

Vector Space Model (VSM) is widely used to represent documents and web pages. It is simple and easy to deal computationally, but it also oversimplifies a document into a vector, susceptible to noise, and cannot explicitly represent underlying topics of a document. A matrix representation of document is proposed in this paper: rows represent distinct terms and columns represent cohesive segments. The matrix model views a document as a set of segments, and each segment is a probability distribution over a limited number of latent topics which can be mapped to clustering structures. The latent topic extraction based on the matrix representation of documents is formulated as a constraint optimization problem in which each matrix (i.e., a document) A _i is factorized into a common base determined by non-negative matrices L and R ^T, and a non-negative weight matrix Mi such that the sum of reconstruction error on all documents is minimized. Empirical evaluation demonstrates that it is feasible to use the matrix model for document clustering: (1) compared with vector representation, using matrix representation improves clustering quality consistently, and the proposed approach achieves a relative accuracy improvement up to 66% on the studied datasets; and (2) the proposed method outperforms baseline methods such as k-means and NMF, and complements the state-of-the-art methods like LDA and PLSI. Furthermore, the proposed matrix model allows more refined information retrieval at a segment level instead of at a document level, which enables the return of more relevant documents in information retrieval tasks.