Latent dirichlet allocation
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TLDR
This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.Abstract:
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.read more
Citations
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Proceedings ArticleDOI
Empirical study of topic modeling in Twitter
Liangjie Hong,Brian D. Davison +1 more
TL;DR: It is shown that by training a topic model on aggregated messages the authors can obtain a higher quality of learned model which results in significantly better performance in two real-world classification problems.
Book ChapterDOI
Advances in Collaborative Filtering
Yehuda Koren,Robert M. Bell +1 more
TL;DR: In this paper, the authors survey the recent progress in the field of collaborative filtering and describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field and demonstrate how to utilize temporal models and implicit feedback to extend models accuracy.
Journal ArticleDOI
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification
Gui-Song Xia,Jingwen Hu,Fan Hu,Baoguang Shi,Xiang Bai,Yanfei Zhong,Liangpei Zhang,Xiaoqiang Lu +7 more
TL;DR: The Aerial Image Data Set (AID) as mentioned in this paper is a large-scale data set for aerial scene classification, which contains more than 10,000 aerial images from remote sensing images.
Journal ArticleDOI
Structural topic models for open ended survey responses
Margaret E. Roberts,Brandon M. Stewart,Dustin Tingley,Chris Lucas,Jetson Leder-Luis,Shana Kushner Gadarian,Bethany Albertson,David G. Rand +7 more
TL;DR: The structural topic model makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects, and is illustrated with analysis of text from surveys and experiments.
Proceedings Article
Hierarchical Topic Models and the Nested Chinese Restaurant Process
TL;DR: A Bayesian approach is taken to generate an appropriate prior via a distribution on partitions that allows arbitrarily large branching factors and readily accommodates growing data collections.
References
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