Low-rank and sparse embedding for dimensionality reduction.
Na HanJigang WuYingyi LiangXiaozhao FangWai Keung WongShaohua TengPublished in: Neural Networks (2018)
Keyphrases
- dimensionality reduction
- nonlinear dimensionality reduction
- low rank and sparse
- graph embedding
- structure preserving
- embedding space
- multidimensional scaling
- low rank
- convex optimization
- signal recovery
- high dimensional data
- manifold learning
- low dimensional
- high dimensional
- feature extraction
- principal component analysis
- singular value decomposition
- linear discriminant analysis
- subspace learning
- feature space
- high dimensionality
- data points
- random projections
- feature selection
- pattern recognition
- low rank matrix
- hierarchical bayesian model
- dimension reduction
- data sets
- semi supervised
- machine learning
- transform domain
- sparse representation
- compressed sensing
- image classification