Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Riemannian batch normalization for SPD neural networks   Covariance matrices have attracted attention for machine learning
applications due to their capacity to capture interesting structure in the
data. The main challenge is that one needs to take into account the particular
geometry of the Riemannian manifold of symmetric positive definite (SPD)
matrices they belong to. In the context of deep networks, several architectures
for these matrices have recently been proposed. In our article, we introduce a
Riemannian batch normalization (batchnorm) algorithm, which generalizes the one
used in Euclidean nets. This novel layer makes use of geometric operations on
the manifold, notably the Riemannian barycenter, parallel transport and
non-linear structured matrix transformations. We derive a new
manifold-constrained gradient descent algorithm working in the space of SPD
matrices, allowing to learn the batchnorm layer. We validate our proposed
approach with experiments in three different contexts on diverse data types: a
drone recognition dataset from radar observations, and on emotion and action
recognition datasets from video and motion capture data. Experiments show that
the Riemannian batchnorm systematically gives better classification performance
compared with leading methods and a remarkable robustness to lack of data.

19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg