Low-Dimensional Decomposition of Manifolds in Presence of Outliers.
Mahlagha SedghiGeorge K. AtiaMichael GeorgiopoulosPublished in: MLSP (2019)
Keyphrases
- low dimensional
- data points
- high dimensional
- manifold learning
- high dimensional data
- dimensionality reduction
- euclidean space
- principal component analysis
- dimension reduction
- low dimensional manifolds
- input space
- low dimensional spaces
- vector space
- feature space
- higher dimensional
- nonlinear dimensionality reduction
- computer vision
- graph embedding
- latent space
- lower dimensional
- linear subspace
- multidimensional scaling
- manifold structure
- noisy data
- embedding space
- laplacian eigenmaps
- outlier detection
- pattern recognition
- data streams
- decomposition algorithm
- euclidean distance
- feature extraction
- pairwise distances