Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to algorithms
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Constructing treatment portfolios using affinity propagation
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
A probabilistic approach for learning folksonomies from structured data
Proceedings of the fourth ACM international conference on Web search and data mining
A comparison of unsupervised learning algorithms for gesture clustering
Proceedings of the 6th international conference on Human-robot interaction
Analyzing microblogs with affinity propagation
Proceedings of the First Workshop on Social Media Analytics
Biclustering of expression microarray data using affinity propagation
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Quantifying statistical interdependence, part iii: N 2 point processes
Neural Computation
Linear text segmentation using affinity propagation
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Pedestrian image segmentation via shape-prior constrained random walks
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Getting emotional about news summarization
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
When Amazon Meets Google: Product Visualization by Exploring Multiple Web Sources
ACM Transactions on Internet Technology (TOIT)
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Affinity propagation (AP) was recently introduced as an unsupervised learning algorithm for exemplar-based clustering. We present a derivation of AP that is much simpler than the original one and is based on a quite different graphical model. The new model allows easy derivations of message updates for extensions and modifications of the standard AP algorithm. We demonstrate this by adjusting the new AP model to represent the capacitated clustering problem. For those wishing to investigate or extend the graphical model of the AP algorithm, we suggest using this new formulation since it allows a simpler and more intuitive model manipulation.