Semi-supervised local Fisher discriminant analysis for dimensionality reduction.
Masashi SugiyamaTsuyoshi IdéShinichi NakajimaJun SesePublished in: Mach. Learn. (2010)
Keyphrases
- dimensionality reduction
- semi supervised
- supervised dimensionality reduction
- subspace learning
- semi supervised dimensionality reduction
- kernel trick
- label information
- linear transformation
- metric learning
- manifold learning
- high dimensional data analysis
- graph embedding
- dimensionality reduction methods
- labeled data
- semi supervised learning
- principal component analysis
- high dimensional
- kernel learning
- unlabeled data
- fisher discriminant analysis
- low dimensional
- unsupervised learning
- active learning
- high dimensionality
- feature selection
- input space
- linear discriminant analysis
- linear dimensionality reduction
- semi supervised classification
- least squares
- lower dimensional
- random projections
- preprocessing step
- data representation
- dimension reduction
- high dimensional data
- euclidean distance
- data points
- multi view
- pairwise
- feature extraction
- feature space
- structure preserving
- pattern recognition
- co training
- sparse representation
- supervised learning
- singular value decomposition
- semi supervised clustering
- principal components
- pattern recognition and machine learning
- multimodal data
- training data
- nonlinear dimensionality reduction
- low rank
- discriminant analysis
- pairwise constraints
- face recognition
- image processing
- data sets