Elements of information theory
Elements of information theory
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
The Journal of Machine Learning Research
A needle in a haystack: local one-class optimization
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Disambiguating Web appearances of people in a social network
WWW '05 Proceedings of the 14th international conference on World Wide Web
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Bregman Bubble Clustering: A Robust, Scalable Framework for Locating Multiple, Dense Regions in Data
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data weaving: scaling up the state-of-the-art in data clustering
Proceedings of the 17th ACM conference on Information and knowledge management
One-class clustering in the text domain
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Dense Neighborhoods on Affinity Graph
International Journal of Computer Vision
One-Class multiple instance learning and applications to target tracking
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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In one-class classification we seek a rule to find a coherent subset of instances similar to a few positive examples in a large pool of instances. The problem can be formulated and analyzed naturally in a rate-distortion framework, leading to an efficient algorithm that compares well with two previous one-class methods. The model can be also be extended to remove background clutter in clustering to improve cluster purity.