Learning Dissimilarities by Ranking: From SDP to QP
Abstract
We consider the problem of learning dissimilarities between points via formulations which preserve a specified ordering between points rather than the numerical values of the dissimilarities. Dissimilarity ranking (d-ranking) learns from instances like "A is more similar to B than C is to D" or "The distance between E and F is larger than that between G and H". Three formulations of d-ranking problems are presented and new algorithms are presented for two of them, one by semidefinite programming (SDP) and one by quadratic programming (QP). Among the novel capabilities of these approaches are out-of-sample prediction and scalability to large problems.
Cite
Text
Ouyang and Gray. "Learning Dissimilarities by Ranking: From SDP to QP." International Conference on Machine Learning, 2008. doi:10.1145/1390156.1390248Markdown
[Ouyang and Gray. "Learning Dissimilarities by Ranking: From SDP to QP." International Conference on Machine Learning, 2008.](https://mlanthology.org/icml/2008/ouyang2008icml-learning/) doi:10.1145/1390156.1390248BibTeX
@inproceedings{ouyang2008icml-learning,
title = {{Learning Dissimilarities by Ranking: From SDP to QP}},
author = {Ouyang, Hua and Gray, Alexander G.},
booktitle = {International Conference on Machine Learning},
year = {2008},
pages = {728-735},
doi = {10.1145/1390156.1390248},
url = {https://mlanthology.org/icml/2008/ouyang2008icml-learning/}
}