Dimension Reduction with Semi-supervised Pairwise Covariance-Preserving Projection.
Xiaoming LiuZhaohui WangJun LiuZhilin FengPublished in: ICIC (3) (2010)
Keyphrases
- dimension reduction
- pairwise
- semi supervised
- unsupervised learning
- semi supervised learning
- labeled data
- pairwise constraints
- principal component analysis
- supervised learning
- feature extraction
- manifold regularization
- manifold learning
- low dimensional
- unlabeled data
- loss function
- random projections
- high dimensional
- partial least squares
- high dimensional data
- high dimensional problems
- high dimensionality
- similarity measure
- singular value decomposition
- high dimensional data analysis
- cluster analysis
- feature space
- data clustering
- dimensionality reduction
- discriminative information
- linear discriminant analysis
- dimension reduction methods
- feature selection
- preprocessing
- metric learning
- nearest neighbor
- pattern recognition
- manifold embedding
- covariance matrix
- labeled and unlabeled data
- spectral clustering
- face recognition
- maximum likelihood
- computer vision