Stochastic neighbor embedding (SNE) for dimension reduction and visualization using arbitrary divergences.
Kerstin BunteSven HaaseMichael BiehlThomas VillmannPublished in: Neurocomputing (2012)
Keyphrases
- dimension reduction
- manifold embedding
- multidimensional scaling
- low dimensional
- principal component analysis
- generative topographic mapping
- feature extraction
- high dimensional
- cluster analysis
- variable selection
- manifold learning
- singular value decomposition
- feature space
- high dimensionality
- feature selection
- random projections
- high dimensional data
- partial least squares
- high dimensional problems
- data mining and machine learning
- graph embedding
- unsupervised learning
- dimensionality reduction
- nearest neighbor
- discriminative information
- self organizing maps
- linear discriminant analysis
- data analysis
- sparse metric learning
- similarity measure
- head pose estimation
- bayesian networks
- least squares
- neural network
- high dimensional data analysis
- vector space