Add to ORKGInference of the document-specific topic distributions in latent Dirichlet allocation (LDA) [2] and decoding in compressed sensing [3] exhibit many similarities. Given a matrix and a noisy observed vector, the goal of both tasks is to recover a sparse vector that, when combined with the matrix, provides a good explanation of the noisy observed data. In the case of LDA, the matrix correspond
Probabilistic topic models such as latent Dirichlet allocation (LDA) are widespread tools to analyse...
Latent Dirichlet Allocation (LDA) is a scheme which may be used to estimate topics and their probabi...
It is well known that the performance of sparse vector recovery algorithms from compressive measurem...
Inference of the document-specific topic distributions in latent Dirichlet allocation (LDA) [2] and ...
— Latent Dirichlet Allocation (LDA) is a probabilistic topic model that aims at organizing, visuali...
Latent Dirichlet Allocation (henceforth LDA) is a statistical model that can be used to represent na...
Topic modeling is a generalization of clustering that posits that observations (words in a document)...
Latent Dirichlet allocation (LDA) is a popular generative model of various objects such as texts and...
Topic modeling is a generalization of clustering that posits that observations (words in a document)...
LDA (Latent Dirichlet Allocation ) and NMF (Non-negative Matrix Factorization) are two popular techn...
Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be update...
Text mining has a wide range of applications in education. In this paper, we review Latent Dirichlet...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of d...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of ...
Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical ana...
Probabilistic topic models such as latent Dirichlet allocation (LDA) are widespread tools to analyse...
Latent Dirichlet Allocation (LDA) is a scheme which may be used to estimate topics and their probabi...
It is well known that the performance of sparse vector recovery algorithms from compressive measurem...
Inference of the document-specific topic distributions in latent Dirichlet allocation (LDA) [2] and ...
— Latent Dirichlet Allocation (LDA) is a probabilistic topic model that aims at organizing, visuali...
Latent Dirichlet Allocation (henceforth LDA) is a statistical model that can be used to represent na...
Topic modeling is a generalization of clustering that posits that observations (words in a document)...
Latent Dirichlet allocation (LDA) is a popular generative model of various objects such as texts and...
Topic modeling is a generalization of clustering that posits that observations (words in a document)...
LDA (Latent Dirichlet Allocation ) and NMF (Non-negative Matrix Factorization) are two popular techn...
Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be update...
Text mining has a wide range of applications in education. In this paper, we review Latent Dirichlet...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of d...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of ...
Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical ana...
Probabilistic topic models such as latent Dirichlet allocation (LDA) are widespread tools to analyse...
Latent Dirichlet Allocation (LDA) is a scheme which may be used to estimate topics and their probabi...
It is well known that the performance of sparse vector recovery algorithms from compressive measurem...