Title :
Optimized intrinsic dimension estimator using nearest neighbor graphs
Author :
Sricharan, Kumar ; Raich, Raviv ; Hero, Alfred O., III
Author_Institution :
Dept. of EECS, Univ. of Michigan, Ann Arbor, MI, USA
Abstract :
We develop an approach to intrinsic dimension estimation based on k-nearest neighbor (kNN) distances. The dimension estimator is derived using a general theory on functionals of kNN density estimates. This enables us to predict the performance of the dimension estimation algorithm. In addition, it allows for optimization of free parameters in the algorithm. We validate our theory through simulations and compare our estimator to previous kNN based dimensionality estimation approaches.
Keywords :
estimation theory; graph theory; optimisation; random processes; k-nearest neighbor distance; kNN based dimensionality estimation approach; nearest neighbor graph; optimized intrinsic dimension estimator; Analysis of variance; Eigenvalues and eigenfunctions; Entropy; Fluctuations; Knee; Laplace equations; Nearest neighbor searches; Principal component analysis; Random variables; State estimation; geodesics; intrinsic dimension; k nearest neighbor; kNN density estimation; manifold learning;
Conference_Titel :
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4244-4295-9
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2010.5494931
