Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Turbo decoding as an instance of Pearl's “belief propagation” algorithm
IEEE Journal on Selected Areas in Communications
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This paper introduces an approach for clustering/classification which is based on the use of local, high-order structure present in the data. For some problems, this local structure might be more relevant for classification than other measures of point similarity used by popular unsupervised and semi-supervised clustering methods. Under this approach, changes in the class label are associated to changes in the local properties of the data. Using this idea, we also pursue to learn how to cluster given examples of clustered data (including from different datasets). We make these concepts formal by presenting a probability model that captures their fundamentals and show that in this setting, learning to cluster is a well defined and tractable task. Based on probabilistic inference methods, we then present an algorithm for computing the posterior probability distribution of class labels for each data point. Experiments in the domain of spatial grouping and functional gene classification are used to illustrate and test these concepts.