Sparse Latent Semantic Analysis

Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The key idea of LSA is to learn a projection matrix that maps the high dimensional vector space representations of documents to a lower dimensional latent space, i.e. so called latent topic space. In this paper, we propose a new model called Sparse LSA, which produces a sparse projection matrix via the 1 regularization. Compared to the traditional LSA, Sparse LSA selects only a small number of relevant words for each topic and hence provides a compact representation of topic-word relationships. Moreover, Sparse LSA is computationally very efficient with much less memory usage for storing the projection matrix. Furthermore, we propose two important extensions of Sparse LSA: group structured Sparse LSA and non-negative Sparse LSA. We conduct experiments on several benchmark datasets and compare Sparse LSA and its ex...

• Type: Journal
• Year: 2011
• Where: SDM
• Authors: Xi Chen, Yanjun Qi, Bing Bai, Qihang Lin, Jaime G. Carbonell
• Keywords: Data Mining, Dimensional Vector Space, Latent Semantic Analysis, SDM 2011, Word Relationships