Randomized subspace learning approach for high dimensional low rank plus sparse matrix decomposition.
Mostafa RahmaniGeorge K. AtiaPublished in: ACSSC (2015)
Keyphrases
- subspace learning
- matrix decomposition
- low rank
- high dimensional
- high dimensional data
- dimensionality reduction
- low rank approximation
- low dimensional
- low rank matrix
- sparse coding
- manifold learning
- singular value decomposition
- sparse representation
- linear combination
- high dimensionality
- semi supervised
- nearest neighbor
- dimension reduction
- latent structure
- kernel matrix
- data points
- missing data
- original data
- principal component analysis
- feature space
- convex optimization
- gene expression data
- matrix factorization
- data representation
- missing values
- machine learning
- feature selection
- data mining
- small number
- learning algorithm
- multidimensional scaling
- signal processing
- image processing
- pattern recognition
- data analysis
- data sets