Combining imaging and clinical data in manifold learning: Distance-based and graph-based extensions of Laplacian Eigenmaps.
Jean-Baptiste FiotJurgen FrippLaurent D. CohenPublished in: ISBI (2012)
Keyphrases
- manifold learning
- clinical data
- laplacian eigenmaps
- semi supervised
- nonlinear dimensionality reduction
- dimensionality reduction
- low dimensional
- high dimensional
- raw data
- high dimensional data
- kernel pca
- knowledge discovery
- subspace learning
- feature extraction
- dimension reduction
- low dimensional manifolds
- sparse representation
- dynamic time warping
- locally linear embedding
- euclidean distance
- distance measure
- neighborhood graph
- pairwise
- manifold structure
- empirical mode decomposition
- medical imaging
- linear discriminant analysis
- semi supervised learning
- principal component analysis
- image processing
- euclidean space
- input space
- machine learning
- support vector machine svm
- nearest neighbor
- pattern recognition