Multiscale stochastic neighbor embedding: Towards parameter-free dimensionality reduction.
John Aldo LeeDiego Hernán Peluffo-OrdóñezMichel VerleysenPublished in: ESANN (2014)
Keyphrases
- parameter free
- dimensionality reduction
- multiscale
- nonlinear dimensionality reduction
- structure preserving
- graph embedding
- locality preserving projections
- embedding space
- multidimensional scaling
- neighborhood preserving
- low dimensional
- categorical data
- high dimensional
- data representation
- outlier detection
- high dimensional data
- low dimensional spaces
- nearest neighbor
- manifold learning
- scale space
- image processing
- wavelet transform
- feature extraction
- edge detection
- locally linear embedding
- principal component analysis
- data points
- latent space
- vector space
- fully automatic
- linear discriminant analysis
- pattern recognition
- image segmentation
- databases
- high dimensionality
- dimensionality reduction methods
- image representation
- unsupervised learning
- video sequences
- linear dimensionality reduction
- metric learning
- principal components
- data hiding
- database
- clustering algorithm
- feature selection