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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Proceedings ArticleDOI
Activity recognition using the velocity histories of tracked keypoints
TL;DR: This work presents an activity recognition feature inspired by human psychophysical performance, based on the velocity history of tracked keypoints, and presents a generative mixture model for video sequences using this feature, and shows that it performs comparably to local spatio-temporal features on the KTH activity recognition dataset.
Proceedings Article
Exponential Family Harmoniums with an Application to Information Retrieval
TL;DR: An alternative two-layer model based on exponential family distributions and the semantics of undirected models is proposed, which performs well on document retrieval tasks and provides an elegant solution to searching with keywords.
Journal ArticleDOI
What are developers talking about? An analysis of topics and trends in Stack Overflow
TL;DR: This article uses latent Dirichlet allocation (LDA), a statistical topic modeling technique, to automatically discover the main topics present in developer discussions of Stack Overflow and analyzes these discovered topics, as well as their relationships and trends over time, to gain insights into the development community.
Journal ArticleDOI
Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning
TL;DR: This work follows a multiple-instance learning approach that iteratively trains the detector and infers the object locations in the positive training images and proposes a window refinement method, which improves the localization accuracy by incorporating an objectness prior.
Proceedings ArticleDOI
Detect Rumors Using Time Series of Social Context Information on Microblogging Websites
TL;DR: A novel approach to capture the temporal characteristics of features related to microblog contents, users and propagation patterns based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information.
References
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Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
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Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
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Harold Jeffreys,R. Bruce Lindsay +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.