On the Dimensionality and Utility of Convolutional Autoencoder's Latent Space Trained with Topology-Preserving Spectral EEG Head-Maps.
Arjun Vinayak ChikkankodLuca LongoPublished in: Mach. Learn. Knowl. Extr. (2022)
Keyphrases
- topology preserving
- latent space
- dimensionality reduction
- lower dimensional
- high dimensional
- feature space
- low dimensional
- high dimensional spaces
- restricted boltzmann machine
- self organizing maps
- level set method
- binary images
- competitive learning
- latent variables
- gaussian process
- principal component analysis
- manifold learning
- generative model
- transfer learning
- high dimensional data
- parameter space
- thinning algorithm
- feature selection
- random projections
- training set
- bayesian framework
- deformation field
- unsupervised learning
- active learning
- euclidean distance
- random variables
- active contours
- feature maps
- probabilistic model
- data analysis
- neural network