A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Penalized Probabilistic Clustering
Neural Computation
Revisiting probabilistic models for clustering with pair-wise constraints
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
Constrained Clustering Via Concavity Cuts
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Semi-supervised graph clustering: a kernel approach
Machine Learning
A global optimization method for semi-supervised clustering
Data Mining and Knowledge Discovery
Using hidden Markov random fields to combine distributional and pattern-based word clustering
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Semi-supervised approach for finding cancer sub-classes on gene expression data
BSB'10 Proceedings of the Advances in bioinformatics and computational biology, and 5th Brazilian conference on Bioinformatics
Data clustering: a user’s dilemma
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
An experimental study of constrained clustering effectiveness in presence of erroneous constraints
Information Processing and Management: an International Journal
Semi-supervised change detection using modified self-organizing feature map neural network
Applied Soft Computing
Semi-supervised projected model-based clustering
Data Mining and Knowledge Discovery
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Classification problems abundantly arise in many computer vision tasks 驴 being of supervised, semi-supervised or unsupervised nature. Even when class labels are not available, a user still might favor certain grouping solutions over others. This bias can be expressed either by providing a clustering criterion or cost function and, in addition to that, by specifying pairwise constraints on the assignment of objects to classes. In this work, we discuss a unifying formulation for labelled and unlabelled data that can incorporate constrained data for model fitting. Our approach models the constraint information by the maximum entropy principle. This modeling strategy allows us (i) to handle constraint violations and soft constraints, and, at the same time, (ii) to speed up the optimization process. Experimental results on face classification and image segmentation indicates that the proposed algorithm is computationallyefficient and generates superior groupings when compared with alternative techniques.