Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering.
Mikhail BelkinPartha NiyogiPublished in: NIPS (2001)
Keyphrases
- laplacian eigenmaps
- nonlinear dimensionality reduction
- manifold learning
- kernel pca
- graph laplacian
- spectral clustering
- high dimensional data
- maximum variance unfolding
- clustering algorithm
- low dimensional
- k means
- dimensionality reduction
- clustering method
- spectral analysis
- multidimensional scaling
- euclidean space
- empirical mode decomposition
- dimensionality reduction methods
- data points
- locally linear embedding
- data clustering
- cluster analysis
- random walk
- dimension reduction
- support vector regression
- feature space
- unsupervised learning
- latent space
- pairwise
- machine learning
- principal components analysis
- dynamic time warping
- multi dimensional
- distance metric
- kernel function