Unsupervised feature extraction by low-rank and sparsity preserving embedding.
Shanhua ZhanJigang WuNa HanJie WenXiaozhao FangPublished in: Neural Networks (2019)
Keyphrases
- low rank
- feature extraction
- sparsity constraints
- semi supervised
- matrix factorization
- linear combination
- missing data
- low rank matrix
- high dimensional data
- convex optimization
- singular value decomposition
- matrix completion
- regularized regression
- dimensionality reduction
- supervised learning
- face recognition
- unsupervised learning
- high order
- dimension reduction
- matrix decomposition
- sparse representation
- high dimensional
- rank minimization
- kernel matrix
- robust principal component analysis
- pattern recognition
- image classification
- minimization problems
- data matrix
- feature vectors
- manifold learning
- principal component analysis
- singular values
- active learning
- unsupervised feature selection
- wavelet transform
- image processing
- tensor factorization
- feature space
- trace norm
- feature selection
- pairwise
- watermarking algorithm
- kernel principal component analysis
- data sets