Dimensionality reduction using non-negative matrix factorization for information retrieval

The vector space model (VSM) is a conventional information retrieval model, which represents a document collection by a term-by-document matrix. Since term-by-document matrices are usually high-dimensional and sparse, they are susceptible to noise and are also difficult to capture the underlying semantic structure. Additionally, the storage and processing of such matrices places great demands on computing resources. Dimensionality reduction is a way to overcome these problems. Principal component analysis (PCA) and singular value decomposition (SVD) are popular techniques for dimensionality reduction based on matrix decomposition, however they contain both positive and negative values in the decomposed matrices. In the work described here, we use non-negative matrix factorization (NMF) for dimensionality reduction of the vector space model. Since matrices decomposed by NMF only contain non-negative values, the original data are represented by only additive, not subtractive, combinations of the basis vectors. This characteristic of parts-based representation is appealing because it reflects the intuitive notion of combining parts to form a whole. Also NMF computation is based on the simple iterative algorithm, it is therefore advantageous for applications involving large matrices. Using the MEDLINE collection, we experimentally showed that NMF offers great improvement over the vector space model