On Fri, Mar 20, 2015 at 11:50:37AM +1100, Zay Maung Maung Aye wrote: > Neighborhood Component Analysis is more cited than ITML.
There is already a pull request on neighborhood component analysis https://github.com/scikit-learn/scikit-learn/issues/3213 A first step of the GSoC could be to complete it. Gaël > On Wed, Mar 18, 2015 at 11:39 PM, Artem <[email protected]> wrote: > Hello everyone > Recently I mentioned metric learning as one of possible projects for this > years' GSoC, and would like to hear your comments. > Metric learning, as follows from the name, is about learning distance > functions. Usually the metric that is learned is a Mahalanobis metric, > thus > the problem reduces to finding a PSD matrix A that minimizes some > functional. > Metric learning is usually done in a supervised way, that is, a user tells > which points should be closer and which should be more distant. It can be > expressed either in form of "similar" / "dissimilar", or "A is closer to B > than to C". > Since metric learning is (mostly) about a PSD matrix A, one can do > Cholesky > decomposition on it to obtain a matrix G to transform the data. It could > lead to something like guided clustering, where we first transform the > data > space according to our prior knowledge of similarity. > Metric learning seems to be quite an active field of research ([1], [2], > [3 > ]). There are 2 somewhat up-to date surveys: [1] and [2]. > Top 3 seemingly most cited methods (according to Google Scholar) are > □ MMC by Xing et al. This is a pioneering work and, according to the > survey #2 > The algorithm used to solve (1) is a simple projected gradient > approach requiring the full > > eigenvalue decomposition of > > M > > at each iteration. This is typically intractable for medium > > and high-dimensional problems > □ Large Margin Nearest Neighbor by Weinberger et al. The survey 2 > acknowledges this method as "one of the most widely-used Mahalanobis > distance learning methods" > LMNN generally performs very well in practice, although it is > sometimes prone to overfitting due to the absence of > regularization, especially in high dimension > □ Information-theoretic metric learning by Davis et al. This one > features > a special kind of regularizer called logDet. > □ There are many other methods. If you guys know that other methods > rock, > let me know. > So the project I'm proposing is about implementing 2nd or 3rd (or both?) > algorithms along with a relevant transformer. > > ------------------------------------------------------------------------------ > Dive into the World of Parallel Programming The Go Parallel Website, > sponsored > by Intel and developed in partnership with Slashdot Media, is your hub for > all > things parallel software development, from weekly thought leadership blogs > to > news, videos, case studies, tutorials and more. Take a look and join the > conversation now. http://goparallel.sourceforge.net/ > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Gael Varoquaux Researcher, INRIA Parietal Laboratoire de Neuro-Imagerie Assistee par Ordinateur NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France Phone: ++ 33-1-69-08-79-68 http://gael-varoquaux.info http://twitter.com/GaelVaroquaux ------------------------------------------------------------------------------ Dive into the World of Parallel Programming The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now. http://goparallel.sourceforge.net/ _______________________________________________ Scikit-learn-general mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
