Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Maximum likelihood competitive learning
Advances in neural information processing systems 2
Elements of information theory
Elements of information theory
1994 Special Issue: Winner-take-all networks for physiological models of competitive learning
Neural Networks - Special issue: models of neurodynamics and behavior
Self-organizing maps
Proceedings of the 1998 conference on Advances in neural information processing systems II
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Flexible discriminant and mixture models
Statistics and neural networks
Mutual Information in Learning Feature Transformations
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Discriminative Clustering: Optimal Contingency Tables by Learning Metrics
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Clustering Gene Expression Data by Mutual Information with Gene Function
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Learning More Accurate Metrics for Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Generative model-based clustering of directional data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A unified framework for model-based clustering
The Journal of Machine Learning Research
Principle of Learning Metrics for Exploratory Data Analysis
Journal of VLSI Signal Processing Systems
Locally linear metric adaptation for semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Information Bottleneck for Gaussian Variables
The Journal of Machine Learning Research
Associative Clustering for Exploring Dependencies between Functional Genomics Data Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Improved learning of Riemannian metrics for exploratory analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Fuzzy classification by fuzzy labeled neural gas
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Growing kernel-based self-organized maps trained with supervised bias
Intelligent Data Analysis
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Semi-supervised graph clustering: a kernel approach
Machine Learning
Neurocomputing
Unsupervised recursive sequence processing
Neurocomputing
Neurocomputing
Computer Science - Research and Development
Clustering with kernel-based self-organized maps trained with supervised bias
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
A unified probabilistic framework for clustering correlated heterogeneous web objects
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Kernel-Based metric adaptation with pairwise constraints
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Fuzzy labeled self-organizing map with label-adjusted prototypes
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
How to "alternatize" a clustering algorithm
Data Mining and Knowledge Discovery
Active selection of clustering constraints: a sequential approach
Pattern Recognition
HMM-based hybrid meta-clustering ensemble for temporal data
Knowledge-Based Systems
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We study the problem of learning groups or categories that are local in the continuous primary space but homogeneous by the distributions of an associated auxiliary random variable over a discrete auxiliary space. Assuming that variation in the auxiliary space is meaningful, categories will emphasize similarly meaningful aspects of the primary space. From a data set consisting of pairs of primary and auxiliary items, the categories are learned by minimizing a Kullback-Leibler divergence-based distortion between (implicitly estimated) distributions of the auxiliary data, conditioned on the primary data. Still, the categories are defined in terms of the primary space. An online algorithm resembling the traditional Hebb-type competitive learning is introduced for learning the categories. Minimizing the distortion criterion turns out to be equivalent to maximizing the mutual information between the categories and the auxiliary data. In addition, connections to density estimation and to the distributional clustering paradigm are outlined. The method is demonstrated by clustering yeast gene expression data from DNA chips, with biological knowledge about the functional classes of the genes as the auxiliary data.