Results 211 to 220 of about 64,619 (262)

LDA-LFM

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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Concept-LDA: Incorporating Babelfy into LDA for aspect extraction

Journal of Information Science, 2019
Latent 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 Ilhan Omurca
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An Improved LDA Approach

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.
Xiao-Yuan Jing, David Zhang 0001, Yuan Yan Tang   +2 more
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Resampling LDA/QR and PCA+LDA for Face Recognition

2005
Principal 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 0003, Songcan Chen
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Optimization of LDA parameters

2020 28th Signal Processing and Communications Applications Conference (SIU), 2020
The aim of topic modeling is to automatically discover topics in large collections of documents. Although it is used in many different fields, the questions of how to eliminate topic instability and how to optimize model parameters are not fully answered yet.
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LDA Revisited

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, Jia Zeng, Mingxuan Yuan, Weixiong Rao, Jianfeng Yan   +4 more
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Dual LDA for Face Recognition

Fundamenta Informaticae, 2004
The complete theory for Fisher and dual discriminant analysis is presented as the background of the novel algorithms. LDA is found as composition of projection onto the singular subspace for within-class normalised data with the projection onto the singular subspace for between-class normalised data.
Skarbek, W, Kucharski, K, Bober, M
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Learning Regularized LDA by Clustering

IEEE Transactions on Neural Networks and Learning Systems, 2014
As a supervised dimensionality reduction technique, linear discriminant analysis has a serious overfitting problem when the number of training samples per class is small. The main reason is that the between- and within-class scatter matrices computed from the limited number of training samples deviate greatly from the underlying ones.
Pang, Yanwei, Wang, Shuang, Yuan, Yuan
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Self-Weighted Unsupervised LDA

IEEE Transactions on Neural Networks and Learning Systems, 2023
As 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 0001, Yunxing Zhang, Rui Zhang 0017   +2 more
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Performance of LDA and DCT models

Journal of Information Science, 2014
The Doubly Correlated Topic Model is a generative probabilistic topic model for automatically identifying topics from the corpus of the text documents. It is a mixed membership model, based on the fact that a document exhibits a number of topics. We used word co-occurrence statistical information for identifying an initial set of topics as posterior ...
Abhishek Singh Rathore, Devshri Roy
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