Discriminative low-rank preserving projection for dimensionality reduction.
Zhonghua LiuJingjing WangGang LiuLin ZhangPublished in: Appl. Soft Comput. (2019)
Keyphrases
- low rank
- dimensionality reduction
- high dimensional data
- singular value decomposition
- semi supervised
- feature extraction
- low dimensional
- principal component analysis
- feature selection
- high dimensional
- kernel matrix
- latent space
- linear combination
- matrix factorization
- matrix completion
- low rank matrix
- rank minimization
- data representation
- linear discriminant analysis
- high dimensionality
- missing data
- pattern recognition
- trace norm
- convex optimization
- kernel learning
- singular values
- dimension reduction
- manifold learning
- matrix decomposition
- subspace learning
- random projections
- low rank matrices
- data points
- metric learning
- image classification
- feature space
- minimization problems
- original data
- high order
- euclidean distance
- sparse representation
- principal components
- image processing
- data sets
- machine learning
- computer vision
- data analysis
- data matrix
- recommender systems
- generative model