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Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
Marcelo Fiori · Pablo Sprechmann · Joshua T Vogelstein · Pablo Muse · Guillermo Sapiro

Sun Dec 08 02:00 PM -- 06:00 PM (PST) @ Harrah's Special Events Center, 2nd Floor

Graph matching is a challenging problem with very important applications in a wide range of fields, from image and video analysis to biological and biomedical problems. We propose a robust graph matching algorithm inspired in sparsity-related techniques. We cast the problem, resembling group or collaborative sparsity formulations, as a non-smooth convex optimization problem that can be efficiently solved using augmented Lagrangian techniques. The method can deal with weighted or unweighted graphs, as well as multimodal data, where different graphs represent different types of data. The proposed approach is also naturally integrated with collaborative graph inference techniques, solving general network inference problems where the observed variables, possibly coming from different modalities, are not in correspondence. The algorithm is tested and compared with state-of-the-art graph matching techniques in both synthetic and real graphs. We also present results on multimodal graphs and applications to collaborative inference of brain connectivity from alignment-free functional magnetic resonance imaging (fMRI) data.

Author Information

Marcelo Fiori (Universidad de la República, Uruguay)
Pablo Sprechmann (Duke University)
Joshua T Vogelstein (Duke University)
Pablo Muse (Universidad de la República, Uruguay)
Guillermo Sapiro (Duke University)

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