Manifold Learning with Intrinsic Distance Estimation Using Kernelized Linear Model for Metric Tensors.
Katsuhiko KojimaYoshifumi KusunokiKeiji TatsumiPublished in: SMC (2020)
Keyphrases
- manifold learning
- linear model
- distance estimation
- tensor voting
- geodesic distance
- similarity search
- embedding space
- least squares
- dimensionality reduction
- high dimensional
- metric space
- riemannian manifolds
- low dimensional
- regression model
- diffusion maps
- semi supervised
- dimension reduction
- high dimensional data
- feature extraction
- nonlinear models
- nonlinear dimensionality reduction
- underlying manifold
- metric learning
- geometric structure
- sparse representation
- pattern recognition
- multiscale
- manifold structure
- distance metric
- distance measure
- euclidean space
- pairwise
- distance function
- computer vision
- kernel function
- unsupervised learning
- image classification
- principal component analysis
- active learning
- preprocessing
- training data
- clustering algorithm