Projects that are tagged with metric learning.

Logo Sparse Compositional Metric Learning v1.1

by bellet - August 16, 2015, 16:41:20 CET [ BibTeX BibTeX for corresponding Paper Download ] 2523 views, 909 downloads, 2 subscriptions

About: Scalable learning of global, multi-task and local metrics from data


Various minor bug fixes and improvements. The basis and triplet generation now fully supports with datasets with very small classes and arbitrary labels (no need to be consecutive or positive). The computational and memory efficiency of the code when data is high dimensional has been largely improved, and we generate a rectangular (smaller) projection matrix when the number of selected basis is smaller than the dimension. K-NN classification with local metrics has been optimized and made significantly less costly in both time and memory.

Logo GESL v1.01

by bellet - May 15, 2015, 11:54:04 CET [ BibTeX BibTeX for corresponding Paper Download ] 2222 views, 884 downloads, 1 subscription

About: Learning string edit distance / similarity from data


Added datasets used in the experiments of the paper

Logo lomo feature extraction and xqda metric learning for person reidentification 1.0

by openpr_nlpr - May 6, 2015, 11:38:32 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1418 views, 225 downloads, 3 subscriptions

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About: This MATLAB package provides the LOMO feature extraction and the XQDA metric learning algorithms proposed in our CVPR 2015 paper. It is fast, and effective for person re-identification. For more details, please visit


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Logo Hub Miner 1.1

by nenadtomasev - January 22, 2015, 16:33:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2577 views, 530 downloads, 2 subscriptions

About: Hubness-aware Machine Learning for High-dimensional Data

  • BibTex support for all algorithm implementations, making all of them easy to reference (via algref package).

  • Two more hubness-aware approaches (meta-metric-learning and feature construction)

  • An implementation of Hit-Miss networks for analysis.

  • Several minor bug fixes.

  • The following instance selection methods were added: HMScore, Carving, Iterative Case Filtering, ENRBF.

  • The following clustering quality indexes were added: Folkes-Mallows, Calinski-Harabasz, PBM, G+, Tau, Point-Biserial, Hubert's statistic, McClain-Rao, C-root-k.

  • Some more experimental scripts have been included.

  • Extensions in the estimation of hubness risk.

  • Alias and weighted reservoir methods for weight-proportional random selection.