Dirichlet Enhanced Latent Semantic Analysis
نویسندگان
چکیده
In latent semantic analysis (LSA), we aim at modelling a large corpus of high-dimensional discrete data from probabilistic perspective. The Assumption: one data point can be modelled by latent factors, which account for the co-occurrence of items within the data. We are also interested in the clustering structure of the data, which may benefit from the latent factors of the items. For example: In document modelling, the data are document-word pairs. Latent factors: topics for words Data clustering: categories of documents
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تاریخ انتشار 2004