Results 281 to 290 of about 300,940 (321)
LDA Measurement of Droplet Velocities in a High Injection Pressure Diesel Fuel Spray
, 2001 Zhen-zhong Xu +3 more
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Predictive Performance of Raman Spectroscopy in Osteoarthritis: A Systematic Review. [PDF]
J Med SystYesmean M +4 more
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Plano de negócios para empresa produtora de azeite: “Lagar do Olival, Lda.”
João Mesquita Nunes Pina Bicho
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ACM Transactions on Knowledge Discovery from Data, 2021
Linear discriminant analysis (LDA) is one of the important techniques for dimensionality reduction, machine learning, and pattern recognition. However, in many applications, applying the classical LDA often faces the following problems: (1) sensitivity to outliers, (2) absence of local geometric information, and (3) small sample size or matrix ...
Yanni Li +5 more
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Linear discriminant analysis (LDA) is one of the important techniques for dimensionality reduction, machine learning, and pattern recognition. However, in many applications, applying the classical LDA often faces the following problems: (1) sensitivity to outliers, (2) absence of local geometric information, and (3) small sample size or matrix ...
Yanni Li +5 more
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Equivalence between LDA/QR and Direct LDA
International Journal of Cognitive Informatics and Natural Intelligence, 2011Singularity problems of scatter matrices in Linear Discriminant Analysis (LDA) are challenging and have obtained attention during the last decade. Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant analysis (DLDA) are two popular algorithms to solve the singularity problem.
Rong-Hua Li +3 more
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ACM SIGAPP Applied Computing Review, 2021
Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are ...
Tatev Karen Aslanyan, Flavius Frasincar
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Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are ...
Tatev Karen Aslanyan, Flavius Frasincar
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Self-Weighted Unsupervised LDA
IEEE Transactions on Neural Networks and Learning Systems, 2023As a hot topic in unsupervised learning, clustering methods have been greatly developed. However, the model becomes more and more complex, and the number of parameters becomes more and more with the continuous development of clustering methods. And parameter-tuning in most methods is a laborious work due to its complexity and unpredictability.
Xuelong Li, Yunxing Zhang, Rui Zhang
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Proceedings of the VLDB Endowment, 2017
We present LDA*, a system that has been deployed in one of the largest Internet companies to fulfil their requirements of "topic modeling as an internal service" ---relying on thousands of machines, engineers in different sectors submit their data, some are as large as 1.8TB, to LDA* and get results back in hours ...
Lele Yut +3 more
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We present LDA*, a system that has been deployed in one of the largest Internet companies to fulfil their requirements of "topic modeling as an internal service" ---relying on thousands of machines, engineers in different sectors submit their data, some are as large as 1.8TB, to LDA* and get results back in hours ...
Lele Yut +3 more
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Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016
Inference algorithms of latent Dirichlet allocation (LDA), either for small or big data, can be broadly categorized into expectation-maximization (EM), variational Bayes (VB) and collapsed Gibbs sampling (GS). Looking for a unified understanding of these different inference algorithms is currently an important open problem.
Jianwei Zhang +4 more
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Inference algorithms of latent Dirichlet allocation (LDA), either for small or big data, can be broadly categorized into expectation-maximization (EM), variational Bayes (VB) and collapsed Gibbs sampling (GS). Looking for a unified understanding of these different inference algorithms is currently an important open problem.
Jianwei Zhang +4 more
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