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
More filters
Journal ArticleDOI
Review: Which is the best way to organize/classify images by content?
Anna Bosch,X. Munoz,Robert Martí +2 more
TL;DR: A detailed review of some of the most commonly used scene classification approaches, giving the advantages and disadvantages of each methodology.
Proceedings ArticleDOI
A Markov Clustering Topic Model for mining behaviour in video
TL;DR: A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynamic Bayesian Network models and Bayesian topic models, and overcomes their drawbacks on accuracy, robustness and computational efficiency.
Posted Content
Slow Feature Analysis for Human Action Recognition
Zhang Zhang,Dacheng Tao +1 more
TL;DR: Experimental results suggest that the SFA-based approach is able to extract useful motion patterns and improves the recognition performance, requires less intermediate processing steps but achieves comparable or even better performance, and has good potential to recognize complex multiperson activities.
Journal ArticleDOI
Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification
TL;DR: A unified framework to expand short texts based on word embedding clustering and convolutional neural network and semantic cliques via fast clustering is proposed, which validates the effectiveness of the proposed method on two open benchmarks.
Proceedings Article
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
TL;DR: The Bayesian Case Model (BCM) as discussed by the authors is a general framework for Bayesian case-based reasoning and prototype classification and clustering, which learns prototypes, the "quintessential" observations that best represent clusters in a dataset.
References
More filters
Book
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.
Book
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.
Book
Theory of probability
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.