Part 2: Machine LearningInternational audiencePLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement
Probabilistic latent semantic analysis (PLSA) is a method for computing term and document relationsh...
We present an LDA approach to entity disambiguation. Each topic is asso-ciated with a Wikipedia arti...
Given the overwhelming quantities of data generated every day, there is a pressing need for tools th...
Probabilistic Latent Semantic Analysis (PLSA) is an effective technique for information re-trieval, ...
Scalable and effective analysis of large text corpora remains a chal-lenging problem as our ability ...
Probabilistic Latent Semantic Analysis (PLSA) has been successfully applied to many text mining task...
Due to the availability of internet-based abstract services and patent databases, bibliometric analy...
Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling tool for content ana...
Due to the availability of internet-based abstract services and patent databases, bibliometric analy...
Due to the availability of internet-based abstract services and patent databases, bibliometric analy...
The web and image repositories such as FickrTm are the largest image databases in the world. There a...
It is current state of knowledge that our neocortex consists of six layers [10]. We take this knowle...
Many learning problems in real world applications involve rich datasets comprising multiple informat...
In many Web applications, such as blog classification and newsgroup classification, labeled data are...
This article presents a probabilistic generative model for text based on semantic topics and syntact...
Probabilistic latent semantic analysis (PLSA) is a method for computing term and document relationsh...
We present an LDA approach to entity disambiguation. Each topic is asso-ciated with a Wikipedia arti...
Given the overwhelming quantities of data generated every day, there is a pressing need for tools th...
Probabilistic Latent Semantic Analysis (PLSA) is an effective technique for information re-trieval, ...
Scalable and effective analysis of large text corpora remains a chal-lenging problem as our ability ...
Probabilistic Latent Semantic Analysis (PLSA) has been successfully applied to many text mining task...
Due to the availability of internet-based abstract services and patent databases, bibliometric analy...
Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling tool for content ana...
Due to the availability of internet-based abstract services and patent databases, bibliometric analy...
Due to the availability of internet-based abstract services and patent databases, bibliometric analy...
The web and image repositories such as FickrTm are the largest image databases in the world. There a...
It is current state of knowledge that our neocortex consists of six layers [10]. We take this knowle...
Many learning problems in real world applications involve rich datasets comprising multiple informat...
In many Web applications, such as blog classification and newsgroup classification, labeled data are...
This article presents a probabilistic generative model for text based on semantic topics and syntact...
Probabilistic latent semantic analysis (PLSA) is a method for computing term and document relationsh...
We present an LDA approach to entity disambiguation. Each topic is asso-ciated with a Wikipedia arti...
Given the overwhelming quantities of data generated every day, there is a pressing need for tools th...