Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Comparing images using color coherence vectors
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Data clustering using a model granular magnet
Neural Computation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adjustment Learning and Relevant Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning a kernel function for classification with small training samples
ICML '06 Proceedings of the 23rd international conference on Machine learning
Kernel-based distance metric learning for content-based image retrieval
Image and Vision Computing
Learning distance function by coding similarity
Proceedings of the 24th international conference on Machine learning
BoostCluster: boosting clustering by pairwise constraints
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
Learning Similarity Measures from Pairwise Constraints with Neural Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Distributed and Incremental Clustering Based on Weighted Affinity Propagation
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
A scalable kernel-based algorithm for semi-supervised metric learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Semi-supervised clustering using similarity neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Image classification from small sample, with distance learning and feature selection
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Joint learning of labels and distance metric
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Identifying Join Candidates in the Cairo Genizah
International Journal of Computer Vision
Learning from pairwise constraints by Similarity Neural Networks
Neural Networks
Kernel-Based metric adaptation with pairwise constraints
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
A new framework for dissimilarity and similarity learning
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Similarity boosting for label noise tolerance in protein-chemical interaction prediction
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Predicting protein-peptide binding affinity by learning peptide-peptide distance functions
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
LIMSI: learning semantic similarity by selecting random word subsets
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Pairwise support vector machines and their application to large scale problems
The Journal of Machine Learning Research
Active selection of clustering constraints: a sequential approach
Pattern Recognition
Learning bilinear model for matching queries and documents
The Journal of Machine Learning Research
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The performance of graph based clustering methods critically depends on the quality of the distance function used to compute similarities between pairs of neighboring nodes. In this paper we learn distance functions by training binary classifiers with margins. The classifiers are defined over the product space of pairs of points and are trained to distinguish whether two points come from the same class or not. The signed margin is used as the distance value. Our main contribution is a distance learning method (DistBoost), which combines boosting hypotheses over the product space with a weak learner based on partitioning the original feature space. Each weak hypothesis is a Gaussian mixture model computed using a semi-supervised constrained EM algorithm, which is trained using both unlabeled and labeled data. We also consider SVM and decision trees boosting as margin based classifiers in the product space. We experimentally compare the margin based distance functions with other existing metric learning methods, and with existing techniques for the direct incorporation of constraints into various clustering algorithms. Clustering performance is measured on some benchmark databases from the UCI repository, a sample from the MNIST database, and a data set of color images of animals. In most cases the DistBoost algorithm significantly and robustly outperformed its competitors.