A geometric approach to the problem of reconstruction of the sample behavior in hidden dimensions
A. Vinogradov, Yu. P. Laptin · Pattern Recognition and Image Analysis · 2010
We investigate a direct geometric approach to the problem of reconstruction of the behavior of a sample of hidden dimensions. A method for an improved description of cluster sampling, based on the interpretation of nonlinearities in the empirical distribution of both local projections of a uniform distribution on a smooth manifold, defined in the hidden dimension, is given. This method can be used to resolve a number of critical features in the empirical distributions. The a priori assumptions under which many variants of reconstruction of sampling behavior in the hidden dimensions are limited are considered.