Sparse Low-Rank Preserving Projection for Dimensionality Reduction.
Zhonghua LiuJingjing WangGang LiuJiexin PuPublished in: IEEE Access (2019)
Keyphrases
- low rank
- dimensionality reduction
- high dimensional data
- low rank matrix
- singular value decomposition
- rank minimization
- low rank subspace
- low rank matrices
- high dimensional
- nuclear norm
- robust principal component analysis
- low rank representation
- kernel matrices
- group sparsity
- sparse representation
- random projections
- low rank approximation
- matrix completion
- regularized regression
- kernel matrix
- low dimensional
- matrix decomposition
- matrix factorization
- linear combination
- high dimensionality
- missing data
- tensor decomposition
- principal component analysis
- pattern recognition
- convex optimization
- manifold learning
- data representation
- feature extraction
- kernel learning
- high order
- data points
- subspace learning
- singular values
- minimization problems
- nearest neighbor
- semi supervised
- data matrix
- sparse coding
- feature selection
- original data
- kernel pca
- image processing
- low rank and sparse
- sparse matrix
- feature space
- metric learning
- machine learning
- data analysis
- data sets