Title :
A nonlinear filtering method for geometric subspace tracking
Author :
Srivastava, Anuj
Author_Institution :
Dept. of Stat., Florida State Univ., Tallahassee, FL, USA
Abstract :
We formulate the problem of tracking principal subspaces as a problem in nonlinear filtering. The subspaces are represented by their complex projection-matrices, and moving subspaces correspond to trajectories on the Grassmann manifold. Taking a Bayesian approach, we impose a smoothness prior on the subspace rotation. Combining ideas from importance sampling and sequential methods, we apply a recursive Monte Carlo approach to solving for MMSE estimates
Keywords :
Bayes methods; Monte Carlo methods; filtering theory; importance sampling; least mean squares methods; nonlinear filters; tracking filters; Bayesian approach; Grassmann manifold; MMSE estimates; complex projection-matrices; geometric subspace tracking; importance sampling; nonlinear filtering method; recursive Monte Carlo approach; sequential methods; smoothness prior; subspace rotation; Bayesian methods; Filtering; Image analysis; Monte Carlo methods; Principal component analysis; Recursive estimation; Signal analysis; Statistics; Time varying systems; White noise;
Conference_Titel :
Sensor Array and Multichannel Signal Processing Workshop. 2000. Proceedings of the 2000 IEEE
Conference_Location :
Cambridge, MA
Print_ISBN :
0-7803-6339-6
DOI :
10.1109/SAM.2000.878060
