title: Using SVD on Clusters to Improve Precision of Interdocument Similarity Measure.
abstract: Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.
authors: Center on Big Data Sciences, Beijing University of Chemical Technology, Beijing 100039, China.
authors: Wen
authors: 0000-0002-6216-2262
authors: W
authors: Zhang
authors: Center on Big Data Sciences, Beijing University of Chemical Technology, Beijing 100039, China.
authors: Fan
authors: F
authors: Xiao
authors: Center on Big Data Sciences, Beijing University of Chemical Technology, Beijing 100039, China.
authors: Bin
authors: B
authors: Li
authors: Institute of Policy and Management, Chinese Academy of Sciences, Beijing 100190, China.
authors: Siguang
authors: S
authors: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D006801:Humans; D016247:Information Storage and Retrieval; D016014:Linear Models; D012660:Semantics