Dimensionality Reduction with Adaptive Kernels

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Dimensionality Reduction with Adaptive Kernels
1 A kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. In this work, we propose a kind of new kernel, called Locality-Adaptive-Kernel (LAKE), which adaptively measures the data similarity by considering the geometrical structure of the data set. In theory, we prove that the LAKE is a special marginalized kernel; and intuitively, when the local kernel in LAKE is constrained to be linear, it has the explicit semantic of merging multiple local linear analyzers into a single global nonlinear one. We show in a toy problem that the kernel principal component analysis with LAKE well captures the intrinsic nonlinear principal curve of the data set. Moreover, a large set of experiments are presented to verify that the classification performance is sensitive to the kernel variation; and the extensive face recognition experiments on different databases demonstrate that KPCA and KDA based on LAKE...
Shuicheng Yan, Xiaoou Tang
Added 09 Nov 2009
Updated 09 Nov 2009
Type Conference
Year 2006
Where ICPR
Authors Shuicheng Yan, Xiaoou Tang
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