Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Robust one-class clustering using hybrid global and local search
ICML '05 Proceedings of the 22nd international conference on Machine learning
Neural Computation
Bregman bubble clustering: A robust framework for mining dense clusters
ACM Transactions on Knowledge Discovery from Data (TKDD)
A rate-distortion one-class model and its applications to clustering
Proceedings of the 25th international conference on Machine learning
One-class clustering in the text domain
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning to identify unexpected instances in the test set
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
Isolating top-k dense regions with filtration of sparse background
Pattern Recognition Letters
Metric anomaly detection via asymmetric risk minimization
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Integrating local one-class classifiers for image retrieval
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Learning from positive and unlabeled examples with different data distributions
ECML'05 Proceedings of the 16th European conference on Machine Learning
Fitting the smallest enclosing bregman ball
ECML'05 Proceedings of the 16th European conference on Machine Learning
One-Class multiple instance learning and applications to target tracking
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
CopyCatch: stopping group attacks by spotting lockstep behavior in social networks
Proceedings of the 22nd international conference on World Wide Web
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This paper addresses the problem of finding a small and coherent subset of points in a given data. This problem, sometimes referred to as one-class or set covering, requires to find a small-radius ball that covers as many data points as possible. It rises naturally in a wide range of applications, from finding gene-modules to extracting documents' topics, where many data points are irrelevant to the task at hand, or in applications where only positive examples are available. Most previous approaches to this problem focus on identifying and discarding a possible set of outliers. In this paper we adopt an opposite approach which directly aims to find a small set of coherently structured regions, by using a loss function that focuses on local properties of the data. We formalize the learning task as an optimization problem using the Information-Bottleneck principle. An algorithm to solve this optimization problem is then derived and analyzed. Experiments on gene expression data and a text document corpus demonstrate the merits of our approach.