Machine Learning
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
The complexity of non-hierarchical clustering with instance and cluster level constraints
Data Mining and Knowledge Discovery
Spectral clustering with inconsistent advice
Proceedings of the 25th international conference on Machine learning
Boosting Clustering by Active Constraint Selection
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
Constrained spectral embedding for K-way data clustering
Pattern Recognition Letters
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As A.I. algorithms are applied to more complex domains that involve high dimensional data sets there is a need to more saliently represent the data. However, most dimension reduction approaches are driven by objective functions that may not or only partially suit the end users requirements. In this work, we show how to incorporate general-purpose domain expertise encoded as a graph into dimension reduction in way that lends itself to an elegant generalized eigenvalue problem. We call our approach Graph-Driven Constrained Dimension Reduction via Linear Projection (GCDR-LP) and show that it has several desirable properties.