Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction.
Masashi SugiyamaTsuyoshi IdéShinichi NakajimaJun SesePublished in: PAKDD (2008)
Keyphrases
- dimensionality reduction
- semi supervised
- supervised dimensionality reduction
- subspace learning
- semi supervised dimensionality reduction
- kernel trick
- label information
- manifold learning
- dimensionality reduction methods
- semi supervised learning
- graph embedding
- low dimensional
- kernel learning
- high dimensional data analysis
- fisher discriminant analysis
- metric learning
- high dimensional
- unlabeled data
- high dimensional data
- principal component analysis
- linear transformation
- labeled data
- pattern recognition
- least squares
- high dimensionality
- data points
- pattern recognition and machine learning
- linear discriminant analysis
- regression algorithm
- lower dimensional
- feature selection
- random projections
- unsupervised learning
- supervised learning
- active learning
- principal components
- pairwise constraints
- semi supervised classification
- pairwise
- data representation
- multi view
- nonlinear dimensionality reduction
- input space
- co training
- preprocessing step
- semi supervised clustering
- structure preserving
- linear dimensionality reduction
- euclidean distance
- feature space
- feature extraction
- dimension reduction
- locality preserving projections
- sparse representation