KEOPS: KErnels Organized into PyramidS

Abstract : Data representation is a crucial issue in signal processing and machine learning. In this work, we propose to guide the learning process with a prior knowledge describing how similarities between examples are organized. This knowledge is encoded in a tree structure that represents nested groups of similarities that are the pyramids of kernels. We propose a framework that learns a Support Vector Machine (SVM) on pyramids of arbitrary heights and identifies the relevant groups of similarities groups are relevant for classifying the examples. A weighted combination of (groups of) similarities is learned jointly with the SVM parameters, by optimizing a criterion that is shown to be an equivalent formulation regularized with a mixed norm of the original fitting problem. Our approach is illustrated on a Brain Computer Interfaces classification problem.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-00979394
Contributor : Marie Szafranski <>
Submitted on : Tuesday, April 15, 2014 - 7:22:35 PM
Last modification on : Monday, October 28, 2019 - 10:50:21 AM

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Marie Szafranski, Yves Grandvalet. KEOPS: KErnels Organized into PyramidS. IEEE 2014 International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014), May 2014, Firenze, Italy. pp.8262--8266, ⟨10.1109/ICASSP.2014.6855212⟩. ⟨hal-00979394⟩

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