Graph Kernels

Rupp, Matthias

doi:10.1002/9781118346990.ch8

Statistical and Machine Learning Approaches for Network Analysis 2012

DOI: 10.1002/9781118346990.ch8

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Graph Kernels

Matthias Rupp

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2015

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Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition

Bouchaffra

2015

IEEE Trans. Neural Netw. Learning Syst.

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We introduce a novel formalism that performs dimensionality reduction and captures topological features (such as the shape of the observed data) to conduct pattern classification. This mission is achieved by: 1) reducing the dimension of the observed variables through a kernelized radial basis function technique and expressing the latent variables probability distribution in terms of the observed variables; 2) disclosing the data manifold as a 3-D polyhedron via the α -shape constructor and extracting topological features; and 3) classifying a data set using a mixture of multinomial distributions. We have applied our methodology to the problem of age-invariant face recognition. Experimental results obtained demonstrate the efficiency of the proposed methodology named nonlinear topological component analysis when compared with some state-of-the-art approaches.

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Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition

Bouchaffra

2015

IEEE Trans. Neural Netw. Learning Syst.

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Graph Kernels

Cited by 1 publication

References 64 publications

Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition

Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition

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