Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data

DOI10.1016/j.patcog.2010.02.022zbMath1207.68261OpenAlexW2032942379MaRDI QIDQ975219

Mahdieh Soleymani Baghshah, Saeed Bagheri Shouraki

Publication date: 8 June 2010

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2010.02.022

zbMATH Keywords

kerneloptimization problemtopological structuremetric learningnon-linearsemi-supervised clusteringpositive and negative constraints

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10) Data structures (68P05)

Related Items (11)

Active constrained fuzzy clustering: a multiple kernels learning approach ⋮ Learning a metric when clustering data points in the presence of constraints ⋮ Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions ⋮ Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition ⋮ A novel multiset integrated canonical correlation analysis framework and its application in feature fusion ⋮ Semi-supervised clustering with discriminative random fields ⋮ Constrained large margin local projection algorithms and extensions for multimodal dimensionality reduction ⋮ Information-Theoretic Semi-Supervised Metric Learning via Entropy Regularization ⋮ Fusion of geochemical and remote-sensing data for lithological mapping using random forest metric learning ⋮ Pairwise constraints based multiview features fusion for scene classification ⋮ Supervised distance metric learning through maximization of the Jeffrey divergence

Cites Work

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• A generalized Foley–Sammon transform based on generalized fisher discriminant criterion and its application to face recognition
• Mixture Modeling with Pairwise, Instance-Level Class Constraints
• Algorithmic Learning Theory

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