Hyperspectral image compressed sensing via low-rank and joint-sparse matrix recovery.
Mohammad GolbabaeePierre VandergheynstPublished in: ICASSP (2012)
Keyphrases
- low rank
- compressed sensing
- sparse matrix
- random projections
- high dimensional data
- dimensionality reduction
- singular value decomposition
- image reconstruction
- linear combination
- convex optimization
- sparse representation
- dimension reduction
- matrix factorization
- missing data
- semi supervised
- low dimensional
- compressive sensing
- high dimensionality
- high order
- original data
- random sampling
- data points
- principal component analysis
- hash functions
- least squares
- feature space
- high dimensional
- natural images
- pairwise
- high resolution
- data sets
- pattern recognition
- markov random field
- multiscale
- data matrix
- rows and columns
- learning algorithm