Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An Introduction to Variational Methods for Graphical Models
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
The Journal of Machine Learning Research
The MERL motion detector dataset
Proceedings of the 2007 workshop on Massive datasets
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Recovering Social Networks From Massive Track Datasets
WACV '08 Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision
On smoothing and inference for topic models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Activity recognition using temporal evidence theory
Journal of Ambient Intelligence and Smart Environments
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
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This paper presents a novel way to perform probabilistic modeling of occupancy patterns from a sensor network. The approach is based on the Latent Dirichlet Allocation (LDA) model. The application of the LDA model is shown using a real dataset of occupancy logs from the sensor network of a modern office building. LDA is a generative and unsupervised probabilistic model for collections of discrete data. Continuous sequences of just binary sensor readings are segmented together in order to build the dataset discrete data (bag-of-words). Then, these bag-of-words are used to train the model with a fixed number of topics, also known as routines. Preliminary obtained results state that the LDA model successfully found latent topics over all rooms and therefore obtain the dominant occupancy patterns or routines on the sensor network.