Clifford Manifold Learning for Nonlinear Dimensionality Reduction

The methods of manifold dimensionalityreduction proposed recently are just for single dimensionaldata and signals. So this paper proposed the general framework of Clifford manifold learning, and solved the problem of relationship between different dimensional signaland data using eigenmapping in local coordinate. We alsomentioned the nonlinear dimensionality reduction analysesmethod based on Clifford algebra, and established the homogeneous analyses model for Clifford nonlinear manifoldwith hybrid dimensional signals. The experiment and comparison proved the effciency of our method for nonlineardimensionality reduction of hybrid dimensional signals.