Joint Sparse and Manifold Learning with Low-rank Embedding for Feature Extraction.
Qianqian ZhangChunman YanPublished in: ICDIP (2023)
Keyphrases
- manifold learning
- low rank
- nonlinear dimensionality reduction
- high dimensional data
- low rank matrix
- semi supervised
- feature extraction
- rank minimization
- high dimensional
- sparse representation
- dimensionality reduction
- low dimensional
- dimension reduction
- singular value decomposition
- locality preserving projections
- discriminant embedding
- geodesic distance
- matrix factorization
- latent space
- convex optimization
- missing data
- manifold structure
- linear combination
- random projections
- high order
- subspace learning
- dictionary learning
- kernel matrix
- locally linear embedding
- multidimensional scaling
- singular values
- nearest neighbor
- high dimensionality
- linear discriminant analysis
- data sets
- image classification
- pattern recognition
- data points
- feature vectors
- vector space
- data analysis
- higher order
- neural network
- image processing
- dimensionality reduction methods
- pairwise
- computer vision
- riemannian manifolds
- active learning
- small number
- sparse coding
- face recognition
- principal component analysis
- feature selection
- data mining
- support vector machine svm