Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems
The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Simulation Framework for the Autobahn Traffic in North Rhine-Westphalia
ACRI '01 Proceedings of the 5th International Conference on Cellular Automata for Research and Industry
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A Vector-Geometry Based Spatial kNN-Algorithm for Traffic Frequency Predictions
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems
IEEE Transactions on Knowledge and Data Engineering
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
Collective traffic forecasting
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Distributed threshold querying of general functions by a difference of monotonic representation
Proceedings of the VLDB Endowment
Ubiquitous knowledge discovery: challenges, techniques, applications
Ubiquitous knowledge discovery: challenges, techniques, applications
Active complex event processing over event streams
Proceedings of the VLDB Endowment
Energy-saving models for wireless sensor networks
Knowledge and Information Systems - Special Issue on Data Warehousing and Knowledge Discovery from Sensors and Streams
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A new class of upper bounds on the log partition function
IEEE Transactions on Information Theory
Pedestrian quantity estimation with trajectory patterns
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
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Modern sensing technology allows us enhanced monitoring of dynamic activities in business, traffic, and home, just to name a few. The increasing amount of sensor measurements, however, brings us the challenge for efficient data analysis. This is especially true when sensing targets can interoperate--in such cases we need learning models that can capture the relations of sensors, possibly without collecting or exchanging all data. Generative graphical models namely the Markov random fields (MRF) fit this purpose, which can represent complex spatial and temporal relations among sensors, producing interpretable answers in terms of probability. The only drawback will be the cost for inference, storing and optimizing a very large number of parameters--not uncommon when we apply them for real-world applications.In this paper, we investigate how we can make discrete probabilistic graphical models practical for predicting sensor states in a spatio-temporal setting. A set of new ideas allows keeping the advantages of such models while achieving scalability. We first introduce a novel alternative to represent model parameters, which enables us to compress the parameter storage by removing uninformative parameters in a systematic way. For finding the best parameters via maximum likelihood estimation, we provide a separable optimization algorithm that can be performed independently in parallel in each graph node. We illustrate that the prediction quality of our suggested method is comparable to those of the standard MRF and a spatio-temporal k-nearest neighbor method, while using much less computational resources.