Reisinger et al 2010: Spherical Topic Models
Contents
Citation
Joseph Reisinger, Austin Waters, Bryan Silverthorn, and Raymond J. Mooney, "Spherical Topic Models", in Proceedings of the 27th International Conference on Machine Learning (ICML 2010), 2010.
Online version
Summary
This is a recent paper that presents Spherical Mixture Model and Variational Inference methods for Latent Dirichlet Allocation (LDA), which is a Bayesian generative model for general problems in Topic modeling. The highlight of this paper is that it models documents as data points in high-dimensional spherical manifold. Like cosine similarity, the model assumes the data is directional, and can be parameterized by cosine distance and other similarity measures in directional statistics. The authors claim that the spherical topic modeling approach outperforms existing models such as LDA.
Brief Description of the method
This paper first formulates the problem as xxx, then discusses the use of and the use of xxx to. In this section, we will first summarize the method they use
Dataset and Experiment Settings
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Experimental Results
The authors performed three major experiments. The first experiment is the . The second experiment explores.
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Related Papers
This paper is related to many papers in three dimensions.
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