Latent dirichlet allocation
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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Journal ArticleDOI
AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification
TL;DR: The Aerial Image data set (AID), a large-scale data set for aerial scene classification, is described to advance the state of the arts in scene classification of remote sensing images and can be served as the baseline results on this benchmark.
Proceedings ArticleDOI
What, where and who? Classifying events by scene and object recognition
Li-Jia Li,Li Fei-Fei +1 more
TL;DR: This paper uses a number of sport games such as snow boarding, rock climbing or badminton to demonstrate event classification and proposes a first attempt to classify events in static images by integrating scene and object categorizations.
Journal ArticleDOI
Textual Analysis in Accounting and Finance: A Survey
Tim Loughran,Bill McDonald +1 more
TL;DR: In this paper, the authors describe the nuances of the textual analysis and some of the tripwires in implementation and highlight the contemporary textual analysis literature and highlight areas of future research.
Journal ArticleDOI
Visual Word Ambiguity
TL;DR: It is demonstrated that explicitly modeling visual word assignment ambiguity improves classification performance compared to the hard assignment of the traditional codebook model, and the proposed model performs consistently.
Journal ArticleDOI
Variational Inference: A Review for Statisticians
TL;DR: Variational inference (VI), a method from machine learning that approximates probability densities through optimization, is reviewed and a variant that uses stochastic optimization to scale up to massive data is derived.
References
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Indexing by Latent Semantic Analysis
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