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Partial Functional Correspondence
Emanuele Rodolà, Luca Cosmo, Michael M. Bronstein +2
In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes. We use perturbation analysis to show how removal of shape parts changes…
MADMM: a generic algorithm for non-smooth optimization on manifolds
Artiom Kovnatsky, Klaus Glashoff, Michael M. Bronstein
Numerous problems in machine learning are formulated as optimization with manifold constraints. In this paper, we propose the Manifold alternating directions method of multipliers…
Robust Principal Component Analysis on Graphs
Nauman Shahid, Vassilis Kalofolias, Xavier Bresson +2
Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well…
Geodesic convolutional neural networks on Riemannian manifolds
Jonathan Masci, Davide Boscaini, Michael M. Bronstein +1
Feature descriptors play a crucial role in a wide range of geometry analysis and processing applications, including shape correspondence, retrieval, and segmentation. In this paper…