Multiple kernel learning: Difference between revisions

Revision as of 20:02, 8 December 2014

Revision as of 20:15, 8 December 2014

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Multiple kernel learning refers to a set of machine learning methods that use a predefined set of [[Kernel method|kernels]] and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing bias due to kernel selection while allowing for more automated machine learning methods, and b) combining data from different sources (e.g. sound and images from a video) that have different notions of similarity and thus require different kernels. Instead of creating a new kernel, multiple kernel algorithms can be used to combine kernels already established for each individual data source.

Multiple kernel learning approaches been used to

==Algorithms==

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===Unsupervised learning===

[[Unsupervised learning|Unsupervised]] multiple kernel learning algorithms have also been advancedproposed <ref>J. TheseZhuang, algorithmsJ. useWang, propertiesS.C.H. ofHoi the& geometryX. Lan. [http://jmlr.csail.mit.edu/proceedings/papers/v20/zhuang11/zhuang11.pdf Unsupervised Multiple Kernel Learning]. JMLR W&CP 20:129–144, 2011</ref>. These algorithms seek to pickoptimize kernelsthe thatkernel areusing the capablefollowing ofprinciples:

*The optimal kernel minimizes the approximation error over the data.

*The optimal error minimizes the distortion over all training data

==MKL Libraries==

Available MKL libraries include

* [http://www.cs.cornell.edu/~ashesh/pubs/code/SPG-GMKL/download.html SPG-GMKL]: A scalable C++ MKL SVM library that can handle a million kernels. <ref>Ashesh Jain, S. V. N. Vishwanathan and Manik Varma. SPG-GMKL: Generalized multiple kernel learning with a million kernels. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Beijing, China, August 2012</ref>

* [http://research.microsoft.com/en-us/um/people/manik/code/GMKL/download.html GMKL]: Generalized Multiple Kernel Learning code in [[MATLAB]], does <math>\ell_1</math> and <math>\ell_2</math> regularization for supervised learning. <ref>M. Varma and B. R. Babu. More generality in efficient multiple kernel learning. In Proceedings of the International Conference on Machine Learning, Montreal, Canada, June 2009</ref>