Algorithms for clustering data
Algorithms for clustering data
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
Neural networks and the bias/variance dilemma
Neural Computation
Complexity optimized data clustering by competitive neural networks
Neural Computation
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Pattern Recognition Using a New Transformation Distance
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A novel optimizing network architecture with applications
Neural Computation
Neural Computation
A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Learning in Neural Networks: Application to Stable Factorization of 2-D Polynomials
Neural Processing Letters
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
An energy function and continuous edit process for graph matching
Neural Computation
Journal of Mathematical Imaging and Vision
Automatic Construction of 2D Shape Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Alignment and Correspondence Using Edit Distance
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Translation-invariant mixture models for curve clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Central Clustering of Attributed Graphs
Machine Learning
Segregation of moving objects using elastic matching
Computer Vision and Image Understanding
A novel optimizing network architecture with applications
Neural Computation
Graph self-organizing maps for cyclic and unbounded graphs
Neurocomputing
Decomposition of two-dimensional shapes for efficient retrieval
Image and Vision Computing
Discriminative Shape Alignment
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
The Journal of Machine Learning Research
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Large sample statistics in the domain of graphs
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Computer Vision and Image Understanding
Segregation of moving objects using elastic matching
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
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Prior knowledge constraints are imposed upon a learning problem in the form of distance measures. Prototypical 2D point sets and graphs are learned by clustering with point-matching and graph-matching distance measures. The point-matching distance measure is approximately invariant under affine transformations---translation, rotation, scale, and shear---and permutations. It operates between noisy images with missing and spurious points. The graph-matching distance measure operates on weighted graphs and is invariant under permutations. Learning is formulated as an optimization problem. Large objectives so formulated (∼ million variables) are efficiently minimized using a combination of optimization techniques---softassign, algebraic transformations, clocked objectives, and deterministic annealing.