Canonical Representations and Computation in SPD Matrices

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4 Dec 2024

Table of Links

Abstract and 1. Introduction

Preliminaries
Proposed Approach

3.1 Notation

3.2 Nueral Networks on SPD Manifolds

3.3 MLR in Structure Spaces

3.4 Neural Networks on Grassmann Manifolds
Experiments
Conclusion and References

B. MLR in Structure Spaces

C. Formulation of MLR from the Perspective of Distances to Hyperplanes

D. Human Action Recognition

E. Node Classification

F. Limitations of our work

G. Some Related Definitions

H. Computation of Canonical Representation

I. Proof of Proposition 3.2

J. Proof of Proposition 3.4

K. Proof of Proposition 3.5

L. Proof of Proposition 3.6

M. Proof of Proposition 3.11

N. Proof of Proposition 3.12

H COMPUTATION OF CANONICAL REPRESENTATIONS

Authors:

(1) Xuan Son Nguyen, ETIS, UMR 8051, CY Cergy Paris University, ENSEA, CNRS, France ([email protected]);

(2) Shuo Yang, ETIS, UMR 8051, CY Cergy Paris University, ENSEA, CNRS, France ([email protected]);

(3) Aymeric Histace, ETIS, UMR 8051, CY Cergy Paris University, ENSEA, CNRS, France ([email protected]).

This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.

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