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which is determined facial velocity information. Then, these two features are integrated and converted to visual words using “bag-of-words” models, and facial expression is represented by a number of visual words. Secondly, the Latent Dirichlet Allocation (LDA) model are utilized to classify different facial expressions such as “anger”, “disgust”, “fear”, “happiness”, “neutral”, “sadness”, and “surprise”. The experimental results show that our proposed method not only performs stably and robustly

Shaoping Zhu

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 1464–1483


Traveling Route Generation Algorithm Based On LDA and Collaborative Filtering

estimation module based on KDE, topic city generation module based on LDA and travel route generation module or recommended city generation module based on collaborative filtering. The data preprocessing and feature extraction module mainly transforms the original data set into a travel route text set through operations such as data cleaning, classification and feature extraction, that is, it conforms to the input format of LDA model, such as the document-content distribution format. The original data

Peng Cui, Yuming Wang, Chunmei Li

International Journal of Advanced Network, Monitoring and Controls , ISSUE 4, 47–62

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