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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
Xi Chen1, Yanjun Qi1, Bing Bai1, Qihang Lin, Jaime G. Carbonell 
01 Jan 2011
TL;DR: A new model called Sparse LSA is proposed, which produces a sparse projection matrix via the `1 regularization and achieves similar performance gains to LSA, but is more efficient in projection computation, storage, and also well explain the topic-word relationships.
Abstract: Latent semantic analysis (LSA), as one of the most popular unsupervised dimension reduction tools, has a wide range of applications in text mining and information retrieval. The key idea of LSA is to learn a projection matrix that maps the high dimensional vector space representations of documents to a lower dimensional latent space, i.e. so called latent topic space. In this paper, we propose a new model called Sparse LSA, which produces a sparse projection matrix via the `1 regularization. Compared to the traditional LSA, Sparse LSA selects only a small number of relevant words for each topic and hence provides a compact representation of topic-word relationships. Moreover, Sparse LSA is computationally very efficient with much less memory usage for storing the projection matrix. Furthermore, we propose two important extensions of Sparse LSA: group structured Sparse LSA and non-negative Sparse LSA. We conduct experiments on several benchmark datasets and compare Sparse LSA and its extensions with several widely used methods, e.g. LSA, Sparse Coding and LDA. Empirical results suggest that Sparse LSA achieves similar performance gains to LSA, but is more efficient in projection computation, storage, and also well explain the topic-word relationships.

40 citations

Posted Content
TL;DR: In this article, the basic latent class model proposed originally by the sociologist Paul F. Lazarfeld for categorical variables is studied and its geometric structure is explained. And the authors draw parallels between the statistical and geometric properties of latent class models and illustrate geometrically the causes of many problems associated with maximum likelihood estimation and related statistical inference.
Abstract: Statistical models with latent structure have a history going back to the 1950s and have seen widespread use in the social sciences and, more recently, in computational biology and in machine learning. Here we study the basic latent class model proposed originally by the sociologist Paul F. Lazarfeld for categorical variables, and we explain its geometric structure. We draw parallels between the statistical and geometric properties of latent class models and we illustrate geometrically the causes of many problems associated with maximum likelihood estimation and related statistical inference. In particular, we focus on issues of non-identifiability and determination of the model dimension, of maximization of the likelihood function and on the effect of symmetric data. We illustrate these phenomena with a variety of synthetic and real-life tables, of different dimension and complexity. Much of the motivation for this work stems from the “100 Swiss Francs” problem, which we introduce and describe in detail.

40 citations

Book ChapterDOI
18 Apr 2009
TL;DR: A class of models that are discriminatively trained to directly map from the word content in a query-document or document- document pair to a ranking score are presented.
Abstract: We present a class of models that are discriminatively trained to directly map from the word content in a query-document or document- document pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, pol- ysemy). However, unlike LSI our models are trained with a supervised signal directly on the task of interest, which we argue is the reason for our superior results. We provide an empirical study on Wikipedia documents, using the links to define document-document or query-document pairs, where we obtain state-of-the-art performance using our method.

40 citations

Journal ArticleDOI
TL;DR: Two types of multi-criteria probabilistic latent semantic analysis algorithms extended from the single-rating version are proposed, inspired by the Bayesian network and linear regression.
Abstract: Nowadays some recommender system researchers have already been engaging multi-criteria that model possible attributes of the item to generate the improved recommendations. However, the statistical machine learning methods successful in the single-rating recommender system have not been investigated in the context of multi-criteria ratings. In this paper, we propose two types of multi-criteria probabilistic latent semantic analysis algorithms extended from the single-rating version. First, the mixture of multi-variate Gaussian distribution is assumed to be the underlying distribution of multi-criteria ratings of each user. Second, we further assume the mixture of the linear Gaussian regression model as the underlying distribution of multi-criteria ratings of each user, inspired by the Bayesian network and linear regression. The experiment results on the Yahoo!Movies ratings data set show that the full multi-variate Gaussian model and the linear Gaussian regression model achieve a stable performance gain over other tested methods.

40 citations

Patent
Jonathan Murray1
27 Jan 2011
TL;DR: In this paper, the authors present a system and method for using modified Latent Semantic Analysis techniques to structure data for efficient search and display, through a process of optimal agglomerative clustering.
Abstract: The disclosed embodiments provide a system and method for using modified Latent Semantic Analysis techniques to structure data for efficient search and display. The present invention creates a hierarchy of clustered documents, representing the topics of a domain corpus, through a process of optimal agglomerative clustering. The output from a search query is displayed in a fisheye view corresponding to the hierarchy of clustered documents. The fisheye view may link to a two-dimensional self-organizing map that represents semantic relationships between documents.

39 citations


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