Random fields representation over manifolds via isometric feature mapping-based dimension reduction.
De-Cheng FengYan-Ping LiangXiaodan RenJie LiPublished in: Comput. Aided Civ. Infrastructure Eng. (2022)
Keyphrases
- manifold learning
- dimension reduction
- feature mapping
- random fields
- low dimensional
- nonlinear dimensionality reduction
- laplacian eigenmaps
- dimensionality reduction
- feature extraction
- high dimensional
- latent space
- principal component analysis
- high dimensional data
- markov random field
- non stationary
- singular value decomposition
- conditional random fields
- feature selection
- maximum entropy
- linear discriminant analysis
- subspace learning
- random projections
- feature space
- parameter estimation
- pattern recognition
- manifold structure
- cluster analysis
- high dimensionality
- unsupervised learning
- semi supervised
- locally linear embedding
- nearest neighbor
- data mining techniques
- euclidean space
- maximum likelihood
- riemannian manifolds
- graphical models
- preprocessing
- sparse representation
- higher order