Compututational Techniques for Real Logarithms of Matrices
SIAM Journal on Matrix Analysis and Applications
Spherical averages and applications to spherical splines and interpolation
ACM Transactions on Graphics (TOG)
Means and Averaging in the Group of Rotations
SIAM Journal on Matrix Analysis and Applications
Approximating the Logarithm of a Matrix to Specified Accuracy
SIAM Journal on Matrix Analysis and Applications
A Class of Intrinsic Schemes for Orthogonal Integration
SIAM Journal on Numerical Analysis
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A Differential Geometric Approach to the Geometric Mean of Symmetric Positive-Definite Matrices
SIAM Journal on Matrix Analysis and Applications
The Scaling and Squaring Method for the Matrix Exponential Revisited
SIAM Journal on Matrix Analysis and Applications
Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial
The Journal of Machine Learning Research
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
The Journal of Machine Learning Research
Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay
Neural Computation
A Theory for Learning by Weight Flow on Stiefel-Grassman Manifold
Neural Computation
Learning independent components on the orthogonal group of matrices by retractions
Neural Processing Letters
On vector averaging over the unit hypersphere
Digital Signal Processing
Covariance, subspace, and intrinsic Crame´r-Rao bounds
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Fast fixed-point neural blind-deconvolution algorithm
IEEE Transactions on Neural Networks
Learning by natural gradient on noncompact matrix-type pseudo-Riemannian manifolds
IEEE Transactions on Neural Networks
A closed-form solution to the problem of averaging over the lie group of special orthogonal matrices
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
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Averaging is a common way to alleviate errors and random fluctuations in measurements and to smooth out data. Averaging also provides a way to merge structured data in a smooth manner. The present paper describes an algorithm to compute averages on matrix Lie groups. In particular, we discuss the case of averaging over the special orthogonal group of matrices, the unitary group of matrices and the group of symmetric positive-definite matrices.