The performance of deterministic matched subspace detectors when using subspaces estimated from noisy, missing data.
Nicholas AsendorfRaj Tejas SuryaprakashRaj Rao NadakuditiPublished in: SSP (2012)
Keyphrases
- missing data
- low rank
- low rank representation
- linear subspace
- low dimensional
- subspace clusters
- incomplete data
- high dimensional data
- noisy data
- lower dimensional
- principal component analysis
- structure from motion
- missing values
- grassmann manifold
- feature space
- canonical correlations
- motion segmentation
- matrix factorization
- high dimensional
- subspace clustering
- affinity matrix
- imprecise data
- subspace learning
- multiple imputation
- rank minimization
- dimensionality reduction
- face recognition
- feature selection
- least squares
- matrix completion
- training set