Conference proceeding
Sparse Latent Semantic Analysis
Society for Industrial and Applied Mathematics. Proceedings of the SIAM International Conference on Data Mining, p.474
01/01/2011
Abstract
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, the researchers 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, they propose two important extensions of Sparse LSA: group structured Sparse LSA and non-negative Sparse LSA.(ProQuest: ... denotes formulae/symbols omitted.)
Details
- Title: Subtitle
- Sparse Latent Semantic Analysis
- Creators
- Xi ChenYanjun QiBing BaiQihang LinJaime Carbonell
- Resource Type
- Conference proceeding
- Publication Details
- Society for Industrial and Applied Mathematics. Proceedings of the SIAM International Conference on Data Mining, p.474
- Publisher
- Society for Industrial and Applied Mathematics
- Language
- English
- Date published
- 01/01/2011
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380555002771
Metrics
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