Abstract
In the manifold learning problem one seeks to discover a smooth low dimensional surface, i. e., a manifold embedded in a higher dimensional linear vector space, based on a set of sample points on the surface. In this paper we consider the Clifford manifold theory for investigating the Multispectral image sample points. We introduced a geometric method to obtain asymptotically consistent estimates of Clifford manifold dimension. In this paper we present a simpler method based on the neighbor graph in the Clifford manifold. The algorithm is applied to standard synthetic Clifford manifolds as well as data sets consisting of Multispectral images.
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