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Why direct LDA is not equivalent to LDA [PDF]
Pattern Recognition, 2006 In this paper, we present counter arguments against the direct LDA algorithm (D-LDA), which was previously claimed to be equivalent to Linear Discriminant Analysis (LDA). We show from Bayesian decision theory that D-LDA is actually a special case of LDA by directly taking the linear space of class means as the LDA solution.
James W. Davis, Hui Gao
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A new LDA formulation with covariates
Communications in Statistics - Simulation and Computation, 2023 The Latent Dirichlet Allocation (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates.
Shimizu, Gilson +2 more
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Transition Metal Ions in Semiconductors: LDA, LDA+U, and Experiment
Acta Physica Polonica A, 2015 and particularly drastic in transition metal (TM) oxides. A considerable improvement is obtained by adding the +U correction for particular atomic orbitals. While the impact of +U terms was extensively discussed for ideal crystals, its impact on the electronic structure of defects is less understood. We analysed the impact of the +U term for Cr, Mn, Fe,
T. Zakrzewski, P. Bogusławski
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, 2020 Linear discriminant analysis (LDA) is a popular tool for classification and dimension reduction. Limited by its linear form and the underlying Gaussian assumption, however, LDA is not applicable in situations where the data distribution is complex. Recently, we proposed a discriminative normalization flow (DNF) model.
Cai, Yunqi, Wang, Dong
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A New Weighted Lda Method In Comparison To Some Versions Of Lda
, 2008 {"references": ["Xiao-Yuan Jing, David Zhang, and Yuan-Yan Tang ,\" An improved\nLDA Approach\", IEEE Transaction on Syatems, Man, And\nCybernetics\u00d4\u00c7\u00f6Part B: Cybernetics, VOL. 34, NO. 5, October 2004.", "Yu Bing. Jin Lianfu. Chen Ping,\"A new LDA-based method for face\nrecognition\", Proceedings of 16th International Conference on ...
Delaram Jarchi, Reza Boostani
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News Classifications with Labeled LDA
Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2015 Automatically categorizing news articles with high accuracy is an important task in an automated quick news system. We present two classifiers to classify news articles based on Labeled Latent Dirichlet Allocation, called LLDA-C and SLLDA-C. To verify classification accuracy we compare classification results obtained by the classifiers with those by ...
Yiqi Bai, Jie Wang
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Bayesian Parameter Estimation in LDA [PDF]
Advances in Computer Science Research, 2015 Latent Dirichlet Allocation (LDA) probabilistic topic model is widely used in text mining, natural language processing and so on. But LDA's mathematical theory is particularly complex, thus it is very difficult to understand LDA for a novice. In order to more quickly and easily learn LDA, and further promote its application, this paper will deeply ...
Z.Z Ji +4 more
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Contextual modeling with labeled multi-LDA
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013 Learning about activities and object affordances from human demonstration are important cognitive capabilities for robots functioning in human environments, for example, being able to classify objects and knowing how to grasp them for different tasks.
Zhang C., Song D., Kjellstrom H.
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LDA Measurements of Steady and Unsteady Flow Through the Induction System of a Heavy Duty Diesel Engine [PDF]
, 1990 Yuanxian Zhu +3 more
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