Abstract
In this paper, we propose a regularization framework for learning geometry-aware kernels. Some existing geometry-aware kernels can be viewed as instances in our framework. Moreover, the proposed framework can be used as a general platform for developing new geometry-aware kernels. We show how multiple sources of information can be integrated in our framework, allowing us to develop more flexible kernels. We present some new kernels based on our framework. The performance of the kernels is evaluated on classification and clustering tasks. The empirical results show that our kernels significantly improve the performance.
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Pan, B., Chen, WS. (2013). Learning Geometry-Aware Kernels in a Regularization Framework. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8047. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40261-6_42
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DOI: https://doi.org/10.1007/978-3-642-40261-6_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40260-9
Online ISBN: 978-3-642-40261-6
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