Results 211 to 220 of about 61,184 (249)
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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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International Journal of Data Warehousing and Mining, 2018
This article describes how text documents are a major data structure in the era of big data. With the explosive growth of data, the number of documents with multi-labels has increased dramatically. The popular multi-label classification technology, which is usually employed to handle multinomial text documents, is sensitive to the noise terms of text ...
Yongjun Zhang +5 more
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This article describes how text documents are a major data structure in the era of big data. With the explosive growth of data, the number of documents with multi-labels has increased dramatically. The popular multi-label classification technology, which is usually employed to handle multinomial text documents, is sensitive to the noise terms of text ...
Yongjun Zhang +5 more
openaire +1 more source
Proceedings of the International Conference on Web Intelligence, 2017
The rapid growth of population has posed a challenge to people for discovering new followees in uni-directional social networks. Intuitively, a user's adoption of others as followees may motivated by her interest as well as social connection. Therefore, it is worth-while to consider both factors at the same time for better recommendations.
Ke Xu +5 more
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The rapid growth of population has posed a challenge to people for discovering new followees in uni-directional social networks. Intuitively, a user's adoption of others as followees may motivated by her interest as well as social connection. Therefore, it is worth-while to consider both factors at the same time for better recommendations.
Ke Xu +5 more
openaire +1 more source