Subspace learning via low rank projections for dimensionality reduction.
Devansh ArpitChetan RamaiahVenu GovindarajuPublished in: BTAS (2016)
Keyphrases
- subspace learning
- low rank
- dimensionality reduction
- high dimensional data
- singular value decomposition
- low dimensional
- principal component analysis
- high dimensional
- manifold learning
- missing data
- high dimensionality
- dimension reduction
- kernel matrix
- pattern recognition
- data representation
- convex optimization
- linear combination
- feature extraction
- linear discriminant analysis
- semi supervised
- matrix factorization
- feature space
- linear subspace
- random projections
- low rank approximation
- background modeling
- data points
- robust face recognition
- unsupervised learning
- feature selection
- subspace clustering
- sparse representation
- metric learning
- machine learning
- euclidean distance
- image processing