Learning on manifolds

Finding the lowest bit of a krazy straw, from the inside

October 21, 2011 — January 26, 2022

functional analysis
Hilbert space
how do science
kernel tricks
machine learning
signal processing
stochastic processes
time series

A placeholder for learning on curved spaces. Not discussed: learning OF the curvature of spaces.

AFAICT this usually boils down to defining an appropriate stochastic process on a manifold.

1 Learning on a given manifold

Learning where there is an a priori manifold seems to also be a usage here? For example the manifold of positive definite matrices is treated in depth in Chikuse and 筑瀬 (2003).

See the work of, e.g.

Manifold optimisation implementations:

There are at least two textbooks online:

2 Information Geometry

The unholy offspring of Fisher information and differential geometry, about which I know little except that it sounds like it should be intuitive. It is probably synonymous with some of the other items on this page if I could sort out all this terminology. See information geometry.

3 Hamiltonian Monte Carlo

You can also discuss Hamiltonian Monte Carlo in this setting. I will not.

4 Langevin Monte Carlo

Girolami et al discuss Langevin Monte Carlo in this context.

5 Natural gradient

See natural gradients.

6 Homogeneous probability

Albert Tarantola’s framing, from his manuscript. How does it relate to information geometry? I don’t know yet. Haven’t had time to read. Also not a common phrasing, which is a danger sign.

7 Incoming

8 References

Absil, Mahony, and Sepulchre. 2008. Optimization algorithms on matrix manifolds.
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Amari, Shun-ichi. 1998. Natural Gradient Works Efficiently in Learning.” Neural Computation.
Amari, Shunʼichi. 2001. Information Geometry on Hierarchy of Probability Distributions.” IEEE Transactions on Information Theory.
Aswani, Bickel, and Tomlin. 2011. Regression on Manifolds: Estimation of the Exterior Derivative.” The Annals of Statistics.
Azangulov, Smolensky, Terenin, et al. 2022. Stationary Kernels and Gaussian Processes on Lie Groups and Their Homogeneous Spaces I: The Compact Case.”
Barndorff-Nielsen. 1987. “Differential and Integral Geometry in Statistical Inference.” In Differential Geometry in Statistical Inference.
Betancourt, Byrne, Livingstone, et al. 2017. The Geometric Foundations of Hamiltonian Monte Carlo.” Bernoulli.
Borovitskiy, Terenin, Mostowsky, et al. 2020. Matérn Gaussian Processes on Riemannian Manifolds.” arXiv:2006.10160 [Cs, Stat].
Boumal. 2013. On Intrinsic Cramér-Rao Bounds for Riemannian Submanifolds and Quotient Manifolds.” IEEE Transactions on Signal Processing.
———. 2020. An Introduction to Optimization on Smooth Manifolds.
Boumal, Mishra, Absil, et al. 2014. Manopt, a Matlab Toolbox for Optimization on Manifolds.” Journal of Machine Learning Research.
Boumal, Singer, Absil, et al. 2014. Cramér-Rao Bounds for Synchronization of Rotations.” Information and Inference.
Carlsson, Ishkhanov, Silva, et al. 2008. On the Local Behavior of Spaces of Natural Images.” International Journal of Computer Vision.
Chen, Silva, Paisley, et al. 2010. Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds.” IEEE Transactions on Signal Processing.
Chikuse, and 筑瀬. 2003. Statistics on Special Manifolds.
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