Add to ORKGLDA (Latent Dirichlet Allocation ) and NMF (Non-negative Matrix Factorization) are two popular techniques to extract topics in a textual document corpus. This paper shows that NMF with Kullback-Leibler divergence approximate the LDA model under a uniform Dirichlet prior, therefore the comparative analysis can be useful to elucidate the implementation of variational inference algorithm for LDA.FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo, Projeto 2011/23689-9
consists of computing the posterior distribution of the unob-served variables, P (pi, z1:N |I) 1. Le...
Topic modeling is a generalization of clustering that posits that observations (words in a document)...
Latent Dirichlet Allocation is a generative technique, the application of which has recently gained ...
Latent Dirichlet Allocation (henceforth LDA) is a statistical model that can be used to represent na...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of ...
It is important to identify the ``correct'' number of topics in mechanisms like Latent Dirichlet All...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of d...
— Latent Dirichlet Allocation (LDA) is a probabilistic topic model that aims at organizing, visuali...
Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in ...
Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in ...
There has been an explosion in the amount of digital text information available in recent years, lea...
Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be update...
Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by ...
Topic modeling is a generalization of clustering that posits that observations (words in a document)...
Non-negative Matrix Factorization (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) ...
consists of computing the posterior distribution of the unob-served variables, P (pi, z1:N |I) 1. Le...
Topic modeling is a generalization of clustering that posits that observations (words in a document)...
Latent Dirichlet Allocation is a generative technique, the application of which has recently gained ...
Latent Dirichlet Allocation (henceforth LDA) is a statistical model that can be used to represent na...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of ...
It is important to identify the ``correct'' number of topics in mechanisms like Latent Dirichlet All...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of d...
— Latent Dirichlet Allocation (LDA) is a probabilistic topic model that aims at organizing, visuali...
Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in ...
Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in ...
There has been an explosion in the amount of digital text information available in recent years, lea...
Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be update...
Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by ...
Topic modeling is a generalization of clustering that posits that observations (words in a document)...
Non-negative Matrix Factorization (NMF, [5]) and Probabilistic Latent Semantic Analysis (PLSA, [4]) ...
consists of computing the posterior distribution of the unob-served variables, P (pi, z1:N |I) 1. Le...
Topic modeling is a generalization of clustering that posits that observations (words in a document)...
Latent Dirichlet Allocation is a generative technique, the application of which has recently gained ...