Fast Robust Subspace Tracking via PCA in Sparse Data-Dependent Noise.
Praneeth NarayanamurthyNamrata VaswaniPublished in: IEEE J. Sel. Areas Inf. Theory (2020)
Keyphrases
- data dependent
- principal component analysis
- high dimensional
- low rank representation
- principal components analysis
- rank minimization
- dimensionality reduction
- robust principal component analysis
- principal components
- low dimensional
- independent component analysis
- generalization bounds
- face recognition
- covariance matrix
- risk bounds
- random projections
- dimension reduction
- high dimensional data
- missing data
- input data
- rademacher complexity
- feature space
- pairwise
- linear subspace
- feature extraction
- sparse pca
- subspace methods
- reproducing kernel hilbert space
- negative matrix factorization
- support vector machine
- computer vision
- linear discriminant analysis
- sparse representation