Title :
Sampling on locally defined principal manifolds
Author :
Bas, Erhan ; Erdogmus, Deniz
Author_Institution :
ECE Dept., Northeastern Univ., Boston, MA, USA
Abstract :
We start with a locally defined principal curve definition for a given probability density function (pdf) and define a pairwise manifold score based on local derivatives of the pdf. Proposed manifold score can be used to check if data pairs lie on the same manifold. We use this score to (i) cluster nonlinear manifolds having irregular shapes, and (ii) (down)sample a selected principal curve with sufficient accuracy sparsely. Our goal is to provide a heuristic-free formulation for principal graph generation and curve parametrization in order to form a basis for a principled principal manifold unwrapping method.
Keywords :
pattern clustering; probability; unsupervised learning; cluster nonlinear manifold; curve parametrization; graph generation; heuristic-free formulation; principal curve definition; probability density function; Eigenvalues and eigenfunctions; Euclidean distance; Image color analysis; Indexes; Kernel; Manifolds; Probability density function; Principal graphs; resampling on manifolds;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2011.5946936