Quasi-Geodesic Neural Learning Algorithms Over the Orthogonal Group: A Tutorial
Simone Fiori; 6(26):743−781, 2005.
The aim of this contribution is to present a tutorial on learning algorithms for a single neural layer whose connection matrix belongs to the orthogonal group. The algorithms exploit geodesics appropriately connected as piece-wise approximate integrals of the exact differential learning equation. The considered learning equations essentially arise from the Riemannian-gradient-based optimization theory with deterministic and diffusion-type gradient. The paper aims specifically at reviewing the relevant mathematics (and at presenting it in as much transparent way as possible in order to make it accessible to readers that do not possess a background in differential geometry), at bringing together modern optimization methods on manifolds and at comparing the different algorithms on a common machine learning problem. As a numerical case-study, we consider an application to non-negative independent component analysis, although it should be recognized that Riemannian gradient methods give rise to general-purpose algorithms, by no means limited to ICA-related applications.
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