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Multi-view Metric Learning in Vector-valued Kernel Spaces

Abstract : We consider the problem of metric learning for multi-view data and present a novel method for learning within-view as well as between-view metrics in vector-valued kernel spaces, as a way to capture multi-modal structure of the data. We formulate two convex optimization problems to jointly learn the metric and the classifier or regressor in kernel feature spaces. An iterative three-step multi-view metric learning algorithm is derived from the optimization problems. In order to scale the computation to large training sets, a block-wise Nyström approximation of the multi-view kernel matrix is introduced. We justify our approach theoretically and experimentally, and show its performance on real-world datasets against relevant state-of-the-art methods.
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Contributor : Riikka Huusari <>
Submitted on : Tuesday, March 20, 2018 - 12:40:53 PM
Last modification on : Monday, December 14, 2020 - 5:28:28 PM
Long-term archiving on: : Tuesday, September 11, 2018 - 5:26:59 AM


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  • HAL Id : hal-01736068, version 1
  • ARXIV : 1803.07821



Riikka Huusari, Hachem Kadri, Cécile Capponi. Multi-view Metric Learning in Vector-valued Kernel Spaces. The 21st International Conference on Artificial Intelligence and Statistics, Apr 2018, Lanzarote, Spain. ⟨hal-01736068⟩



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