A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps.
Benjamin PeherstorferDirk PflügerHans-Joachim BungartzPublished in: Australasian Conference on Artificial Intelligence (2011)
Keyphrases
- laplacian eigenmaps
- dimensionality reduction
- kernel pca
- nonlinear dimensionality reduction
- manifold learning
- high dimensional
- high dimensional data
- data points
- high dimensionality
- low dimensional
- locally linear embedding
- spectral clustering
- unsupervised learning
- sparse representation
- dimensionality reduction methods
- multidimensional scaling
- random projections
- clustering algorithm
- principal component analysis
- k means
- clustering method
- pattern recognition
- subspace learning
- feature extraction
- data clustering
- dimension reduction
- feature space
- preprocessing step
- principal components analysis
- feature selection
- lower dimensional
- input space
- sparse coding
- face recognition
- kernel methods
- cluster analysis
- nearest neighbor
- euclidean space
- kernel matrix
- linear discriminant analysis
- dimensional data
- singular value decomposition
- euclidean distance
- data sets