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Tag refinement by regularized LDA
Proceedings of the 17th ACM international conference on Multimedia, 2009Tagging is nowadays the most prevalent and practical way to make images searchable. However, in reality many tags are irrelevant to image content. To refine the tags, previous solutions usually mine tag relevance relying on the tag similarity estimated right from the corpus to be refined.
Hao Xu +3 more
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Journal of Information Science, 2014
Probabilistic topic models are statistical methods whose aim is to discover the latent structure in a large collection of documents. The intuition behind topic models is that, by generating documents by latent topics, the word distribution for each topic can be modelled and the prior distribution over the topic learned.
Bagheri, Ayoub +2 more
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Probabilistic topic models are statistical methods whose aim is to discover the latent structure in a large collection of documents. The intuition behind topic models is that, by generating documents by latent topics, the word distribution for each topic can be modelled and the prior distribution over the topic learned.
Bagheri, Ayoub +2 more
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On an equivalence between PLSI and LDA
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, 2003Latent Dirichlet Allocation (LDA) is a fully generative approach to language modelling which overcomes the inconsistent generative semantics of Probabilistic Latent Semantic Indexing (PLSI). This paper shows that PLSI is a maximum a posteriori estimated LDA model under a uniform Dirichlet prior, therefore the perceived shortcomings of PLSI can be ...
Mark A. Girolami, Ata Kabán
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A Comparative Study of PCA, LDA and Kernel LDA for Image Classification
2009 International Symposium on Ubiquitous Virtual Reality, 2009Although various discriminant analysis approaches have been used in Content-Based Image Retrieval (CBIR) application, there have been relatively few concerns with kernel-based methods. Furthermore, these CBIR applications still applied discriminant analysis to face images as face recognition did.
Fei Ye, Zhiping Shi, Zhongzhi Shi
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Parallel LDA with Over-Decomposition
2017 IEEE 24th International Conference on High Performance Computing Workshops (HiPCW), 2017Latent Dirichlet Allocation (LDA) is a statistical technique for topic modeling. Prior efforts to parallelize LDA have either used expensive atomic operations or weakened the sampling model to enable parallelization without heavy use of atomics. In this paper, we present a parallel LDA implementation that uses an over-decomposed 2D tiling strategy to ...
Gordon Euhyun Moon +2 more
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WT-LDA: User Tagging Augmented LDA for Web Service Clustering
2013Clustering Web services that groups together services with similar functionalities helps improve both the accuracy and efficiency of the Web service search engines. An important limitation of existing Web service clustering approaches is that they solely focus on utilizing WSDL Web Service Description Language documents.
Liang Chen 0001 +4 more
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Supervised LDA for Image Annotation
2011 IEEE International Conference on Systems, Man, and Cybernetics, 2011Region-based Image Annotation has received increasing attention in recent years. Topic models such as probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) have shown great success in object recognition and localization. In this paper, we introduce a supervised topic model for region-based image annotation.
Qiaojin Guo +3 more
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Biased Parameter Estimation in LDA
2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization, manifold discovery and other application in discrete data. However, LDA alone does not perform well in IR application. We alleviate it by biased parameter estimation, which makes the topics in LDA more independent than standard LDA.
Boqiu Yuan, Yiming Zhou, Lin Li
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Pattern Recognition Letters, 2005
In this paper, we prove that the principal component analysis (PCA) and the linear discriminant analysis (LDA) can be directly implemented in the discrete cosine transform (DCT) domain and the results are exactly the same as the one obtained from the spatial domain. In some applications, compressed images are desirable to reduce the storage requirement.
Weilong Chen, Meng Joo Er, Shiqian Wu
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In this paper, we prove that the principal component analysis (PCA) and the linear discriminant analysis (LDA) can be directly implemented in the discrete cosine transform (DCT) domain and the results are exactly the same as the one obtained from the spatial domain. In some applications, compressed images are desirable to reduce the storage requirement.
Weilong Chen, Meng Joo Er, Shiqian Wu
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18th International Conference on Pattern Recognition (ICPR'06), 2006
We propose an alternative to probability density classifiers based on normal distributions LDA and QDA. Instead of estimating covariance matrices using the standard maximum likelihood estimator we estimate class domains by the minimum volume enclosing ellipsoid (i-MVEE). The i-MVEE is a robust statistic rejecting a specified fraction i of the data. The
Piotr Juszczak +3 more
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We propose an alternative to probability density classifiers based on normal distributions LDA and QDA. Instead of estimating covariance matrices using the standard maximum likelihood estimator we estimate class domains by the minimum volume enclosing ellipsoid (i-MVEE). The i-MVEE is a robust statistic rejecting a specified fraction i of the data. The
Piotr Juszczak +3 more
openaire +1 more source