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Probabilistic latent semantic analysis

About: Probabilistic latent semantic analysis is a research topic. Over the lifetime, 2884 publications have been published within this topic receiving 198341 citations. The topic is also known as: PLSA.


Papers
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Proceedings ArticleDOI
06 Nov 2005
TL;DR: This paper forms a more general nonlinear model, called Nonlinear Latent Space model, to reveal the latent variables of word and visual features more precisely and presents a novel inference strategy for image annotation via Image-Word Embedding (IWE).
Abstract: Latent Semantic Analysis (LSA) has shown encouraging performance for the problem of unsupervised image automatic annotation. LSA conducts annotation by keywords propagation on a linear Latent Space, which accounts for the underlying semantic structure of word and image features. In this paper, we formulate a more general nonlinear model, called Nonlinear Latent Space model, to reveal the latent variables of word and visual features more precisely. Instead of the basic propagation strategy, we present a novel inference strategy for image annotation via Image-Word Embedding (IWE). IWE simultaneously embeds images and words and captures the dependencies between them from a probabilistic viewpoint. Experiments show that IWE-based annotation on the nonlinear latent space outperforms previous unsupervised annotation methods.

15 citations

Proceedings ArticleDOI
14 Jun 2018
TL;DR: This paper presents a survey on different topic modeling techniques which includes LatentSemantic Analysis (LSA), Probabilistic Latent Semantic analysis (PLSA), and Latent Dirichlet Allocation (LDA) along with some of the extensions of LDA.
Abstract: Topic model provides an easy means to analyze huge amount of untagged text as well as other data. A topic can be defined as a group of words that happen to occur together at a greater frequency. Topic models connects words that have similar kind of meanings and differentiate among words with different or multiple meanings. So, topic models in simple words are a set of algorithms that unveil the hidden thematic structure in a document collection. It allows us to order, search and outline different large records of texts. In this paper we present a survey on different topic modeling techniques which includes Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), and Latent Dirichlet Allocation (LDA) along with some of the extensions of LDA. The characteristics, limitations and applications of these topic modeling techniques are also studied and summarized.

15 citations

Journal ArticleDOI
TL;DR: A new sampling-based Bayesian technique, called the DA-T-Gibbs sampler, which relies on the particular latent data structure of latent response models to simplify the computations involved in parameter estimation.
Abstract: This paper introduces a new technique for estimating the parameters of models with continuous latent data. Using the Rasch model as an example, it is shown that existing Bayesian techniques for parameter estimation, such as the Gibbs sampler, are not always easy to implement. Then, a new sampling-based Bayesian technique, called the DA-T-Gibbs sampler, is introduced. The DA-T-Gibbs sampler relies on the particular latent data structure of latent response models to simplify the computations involved in parameter estimation.

15 citations

Journal Article
TL;DR: An object-oriented model in which the semantic features of the UMLS are made available through four major classes for representing Metathesaurus concepts, semantic types, inter-concept relationships and Semantic Network relationships is proposed.
Abstract: Several information models have been developed for the Unified Medical Language System (UMLS). While some models are term-oriented, a knowledge-oriented model is needed for representing semantic locality, i.e. the various semantic links among concepts. We propose an objectoriented model in which the semantic features of the UMLS are made available through four major classes for representing Metathesaurus concepts, semantic types, interconcept relationships and Semantic Network relationships. Additional semantic methods for reducing the complexity of the hierarchical relationships represented in the UMLS are proposed. Implementation details are presented, as well as examples of use. The interest of this approach is discussed.

15 citations

Journal ArticleDOI
TL;DR: A novel updating method for Probabilistic Latent Semantic Analysis (PLSA), called Recursive PLSA, is proposed, and it is demonstrated that the clusters generated by the RPLSA are closer to the ground truth than those created by the other PLSA or LSA updating methods.

15 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202319
202277
202114
202036
201927
201858