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
Survey on categorical data for neural networks
TL;DR: This study provides a starting point for research in determining which techniques for preparing qualitative data for use with neural networks are best, and is the first in-depth look at techniques for working with categorical data in neural networks.
Journal ArticleDOI
Scene Classification Based on the Multifeature Fusion Probabilistic Topic Model for High Spatial Resolution Remote Sensing Imagery
TL;DR: A semantic allocation level (SAL) multifeature fusion strategy based on PTM, namely, SAL-PTM (S AL-pLSA and SAL-LDA) for HSR imagery is proposed, and the experimental results confirmed that SAL- PTM is superior to the single-feature methods and CAT-PTm in the scene classification of H SR imagery.
Generative or Discriminative? Getting the Best of Both Worlds
Julia Lasserre,Christopher M. Bishop,José M. Bernardo,Maria J. Bayarri,James O. Berger,A. P. Dawid,David Heckerman,Adrian F. M. Smith,Mike West +8 more
TL;DR: This paper presents an approach to finding the conditional distribution p(c|x) using a parametric model, and then to determine the parameters using a training set consisting of pairs of input vectors along with their corresponding target output vectors.
Journal ArticleDOI
Unsupervised Object Discovery: A Comparison
TL;DR: The goal of this paper is to discover the objects present in the images by analyzing unlabeled data and searching for re-occurring patterns, and a rigorous framework for evaluating unsupervised object discovery methods is proposed.
Journal ArticleDOI
Negative Binomial Process Count and Mixture Modeling
Mingyuan Zhou,Lawrence Carin +1 more
TL;DR: It is shown that with augmentation and normalization, the NB process and gamma-NB process can be reduced to the Dirichlet process and hierarchical Dirichlets process, respectively, and relationships between various count- and mixture-modeling distributions are revealed.
References
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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.
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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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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.