A class of subspace tracking algorithms based on approximation of the noise-subspace.
Tony GustafssonCraig S. MacInnesPublished in: IEEE Trans. Signal Process. (2000)
Keyphrases
- low dimensional
- subspace clustering
- high dimensional
- test sample
- dimensionality reduction
- subspace learning
- feature space
- principal component analysis
- input data
- canonical correlations
- hilbert space
- linear subspace
- subspace clusters
- locality preserving projections
- eigendecomposition
- subspace methods
- approximation algorithms
- neighboring points
- clustering high dimensional data
- continuous functions
- feature extraction
- lower dimensional
- noisy data
- signal subspace
- kernel based nonlinear