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    Canonical Representations and Computation in SPD Matrices

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    Table of Links

    Abstract and 1. Introduction

    1. Preliminaries

    2. 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

    3. Experiments

    4. Conclusion and References

    A. Notations

    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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    Hyperbole@hyperbole
    Amplifying words and ideas to separate the ordinary from the extraordinary, making the mundane majestic.
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    Topics

    #deep-neural-networks #riemannian-manifolds #spd-manifolds #graph-convolutional-networks #spdnet #manifold-neural-networks #logistic-regression #euclidean-neural-networks

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