Low-Rank Sparse Preserving Projections for Dimensionality Reduction.
Luofeng XieMing YinXiangyun YinYun LiuGuofu YinPublished in: IEEE Trans. Image Process. (2018)
Keyphrases
- low rank
- dimensionality reduction
- high dimensional data
- low rank matrix
- singular value decomposition
- low rank subspace
- rank minimization
- high dimensional
- low rank matrices
- binary matrices
- nuclear norm
- low rank representation
- robust principal component analysis
- sparse representation
- kernel matrices
- random projections
- matrix decomposition
- low dimensional
- regularized regression
- low rank approximation
- matrix factorization
- kernel matrix
- missing data
- tensor decomposition
- data representation
- convex optimization
- matrix completion
- high dimensionality
- linear combination
- principal component analysis
- data points
- singular values
- feature selection
- high order
- binary matrix
- semi supervised
- sparse matrix
- feature space
- subspace learning
- data matrix
- sparse coding
- compressive sensing
- pattern recognition
- trace norm
- subspace clustering
- kernel pca
- kernel learning
- manifold learning
- nearest neighbor
- feature extraction
- affinity matrix
- signal recovery
- low rank and sparse
- data analysis
- discrete tomography
- multidimensional scaling
- data sets
- metric learning
- original data
- pairwise
- machine learning