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Proceedings of the 21st international conference on World Wide Web, 2012
Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for exploring document collections. Because of the increasing prevalence of large datasets, there is a need to improve the scalability of inference for LDA. In this paper, we introduce a novel and flexible large scale topic modeling package in MapReduce (Mr. LDA).
Ke Zhai +3 more
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Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for exploring document collections. Because of the increasing prevalence of large datasets, there is a need to improve the scalability of inference for LDA. In this paper, we introduce a novel and flexible large scale topic modeling package in MapReduce (Mr. LDA).
Ke Zhai +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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International Journal of Grid and High Performance Computing, 2016
Latent Dirichlet Allocation(LDA) is an efficient method of text mining,but applying LDA directly to Chinese micro-blog texts will not work well because micro-blogs are more social, brief, and closely related with each other. Based on LDA, this paper proposes a Micro-blog Relation LDA model (MR-LDA), which takes the relations between Chinese micro-blog ...
Xiongwen Pang +3 more
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Latent Dirichlet Allocation(LDA) is an efficient method of text mining,but applying LDA directly to Chinese micro-blog texts will not work well because micro-blogs are more social, brief, and closely related with each other. Based on LDA, this paper proposes a Micro-blog Relation LDA model (MR-LDA), which takes the relations between Chinese micro-blog ...
Xiongwen Pang +3 more
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IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2004
Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness.
Jing, XY, Zhang, DD, Tang, YY
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Linear discrimination analysis (LDA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in LDA at least three areas of weakness.
Jing, XY, Zhang, DD, Tang, YY
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Concept-LDA: Incorporating Babelfy into LDA for aspect extraction
Journal of Information Science, 2019Latent Dirichlet allocation (LDA) is one of the probabilistic topic models; it discovers the latent topic structure in a document collection. The basic assumption under LDA is that documents are viewed as a probabilistic mixture of latent topics; a topic has a probability distribution over words and each document is modelled on the basis of a bag-of ...
Ekin Ekinci, Sevinç İlhan Omurca
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2007 6th International Conference on Information, Communications & Signal Processing, 2007
Moments and functions of moments are powerful tools in a vast number of fields, particularly image signal processing. In this paper, a method for obtaining a set of orthogonal, noise-robust, and distribution-adaptive moments, called Fishermoments (FM), is presented.
null Pew-Thian Yap +2 more
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Moments and functions of moments are powerful tools in a vast number of fields, particularly image signal processing. In this paper, a method for obtaining a set of orthogonal, noise-robust, and distribution-adaptive moments, called Fishermoments (FM), is presented.
null Pew-Thian Yap +2 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001
In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis). In this communication, we show that this is not always the case.
A.M. Martinez, A.C. Kak
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In the context of the appearance-based paradigm for object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis). In this communication, we show that this is not always the case.
A.M. Martinez, A.C. Kak
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Proceedings of the 26th Annual International Conference on Machine Learning, 2009
Given a large-scale linked document collection, such as a collection of blog posts or a research literature archive, there are two fundamental problems that have generated a lot of interest in the research community. One is to identify a set of high-level topics covered by the documents in the collection; the other is to uncover and analyze the social ...
Yan Liu +2 more
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Given a large-scale linked document collection, such as a collection of blog posts or a research literature archive, there are two fundamental problems that have generated a lot of interest in the research community. One is to identify a set of high-level topics covered by the documents in the collection; the other is to uncover and analyze the social ...
Yan Liu +2 more
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Resampling LDA/QR and PCA+LDA for Face Recognition
2005Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the Small Sample Size (SSS) problem in traditional LDA. When applied to face recognition under varying lighting conditions and different facial expressions, neither method may work robustly.
Jun Liu, Songcan Chen
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1D-LDA verses 2D-LDA in online handwriting recognition
International Conference on Circuits, Communication, Control and Computing, 2014The paper compares the performance of both one-dimensional (ID) and two-dimensional (2D) linear discriminant analysis (LDA) in recognizing online handwritten Kannada characters. The main difference between 1D-LDA and 2D-LDA is the way the data is presented to these tools for dimensionality reduction.
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