Title
Learning Paths from Signature Tensors.
Abstract
Matrix congruence extends naturally to the setting of tensors. We apply methods from tensor decomposition, algebraic geometry, and numerical optimization to this group action. Given a tensor in the orbit of another tensor, we compute a matrix which transforms one to the other. Our primary application is an inverse problem from stochastic analysis: the recovery of paths from their third order signature tensors. We establish identifiability results, both exact and numerical, for piecewise linear paths, polynomial paths, and generic dictionaries. Numerical optimization is applied for recovery from inexact data. We also compute the shortest path with a given signature tensor.
Year
DOI
Venue
2018
10.1137/18M1212331
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS
Keywords
Field
DocType
signature tensors,congruence action,tensor decomposition,identifiability,inverse problems,optimization
Applied mathematics,Matrix congruence,Polynomial,Shortest path problem,Tensor,Identifiability,Matrix (mathematics),Mathematical analysis,Inverse problem,Piecewise linear function,Mathematics
Journal
Volume
Issue
ISSN
40
2
0895-4798
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Max Pfeffer100.34
Anna Seigal261.45
Bernd Sturmfels3926136.85