Going deep in clustering high-dimensional data: deep mixtures of unigrams for uncovering topics in textual data

18 Feb 2019Laura AnderlucciCinzia Viroli

Mixtures of Unigrams (Nigam et al., 2000) are one of the simplest and most efficient tools for clustering textual data, as they assume that documents related to the same topic have similar distributions of terms, naturally described by Multinomials. When the classification task is particularly challenging, such as when the document-term matrix is high-dimensional and extremely sparse, a more composite representation can provide better insight on the grouping structure... (read more)

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