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Article
Regularized principal component analysis
Given a set of signals, a classical construction of an optimal truncatable basis for optimally representing the signals, is the principal component analysis (PCA for short) approach. When the information about...
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Article
Spectral Generalized Multi-dimensional Scaling
Multidimensional scaling (MDS) is a family of methods that embed a given set of points into a simple, usually flat, domain. The points are assumed to be sampled from some metric space, and the map** attempts...
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Chapter and Conference Paper
Non-rigid Shape Correspondence Using Surface Descriptors and Metric Structures in the Spectral Domain
Finding correspondence between non-rigid shapes is at the heart of three-dimensional shape processing. It has been extensively addressed over the last decade, but efficient and accurate correspondence detectio...
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Chapter and Conference Paper
The Laplace-Beltrami Operator: A Ubiquitous Tool for Image and Shape Processing
The ubiquity of the Laplace-Beltrami operator in shape analysis can be seen by observing the wide variety of applications where it has been found to be useful. Here we demonstrate a small subset of such uses w...
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Chapter and Conference Paper
Deformable Shape Retrieval by Learning Diffusion Kernels
In classical signal processing, it is common to analyze and process signals in the frequency domain, by representing the signal in the Fourier basis, and filtering it by applying a transfer function on the Fou...
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Chapter and Conference Paper
Measuring Geodesic Distances via the Uniformization Theorem
According to the Uniformization Theorem any surface can be conformally mapped into a flat domain, that is, a domain with zero Gaussian curvature. The conformal factor indicates the local scaling introduced by suc...
