Improving and characterizing the performance of stochastic matched subspace detectors when using noisy estimated subspaces.
Nicholas AsendorfRaj Rao NadakuditiPublished in: ACSCC (2011)
Keyphrases
- linear subspace
- low dimensional
- high dimensional data
- subspace clusters
- lower dimensional
- principal component analysis
- linear projection
- canonical correlations
- grassmann manifold
- low rank representation
- high dimensional
- subspace clustering
- feature space
- dimensionality reduction
- hilbert space
- stochastic model
- noisy data
- image space
- principal components
- subspace learning
- original data
- noisy environments
- noise free
- object detection
- subspace analysis
- spherical harmonics
- null space
- basis vectors
- subspace methods
- feature selection
- missing data
- data analysis
- feature extraction