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Latent Dirichlet Allocation

Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data, introduced by Blei, Ng, and Jordan in 2003. It treats each document as a mixture of latent topics and each topic as a probability distribution over words, enabling unsupervised discovery of thematic structure across large text corpora. It is one of the most cited papers in machine learning and natural language processing.

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Latent Dirichlet Allocation (LDA — Blei, Ng & Jordan 2003)

Rekod kaedah taksonomik · latent-structure / machine-learning

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. · DOI 10.5555/944919.944937
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84. · DOI 10.1145/2133806.2133826
Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9). Springer. · ISBN 978-0-387-31073-2

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Used in the same domainK-Means Clusteringmachine-suggested · Relational suggestion, not evidence.Same method familyNon-negative Matrix Factorizationmachine-suggested · Relational suggestion, not evidence.See alsoWord2Vecmachine-suggested · Relational suggestion, not evidence.