Neural Network Optimization with Biologically Inspired Low-Dimensional Manifold Learning.
Hieu LeAndrew WoodSylee DandekarPeter ChinPublished in: CSCI (2021)
Keyphrases
- manifold learning
- biologically inspired
- low dimensional
- neural network
- learning rules
- high dimensional
- dimensionality reduction
- high dimensional data
- nonlinear dimensionality reduction
- diffusion maps
- biologically plausible
- principal component analysis
- laplacian eigenmaps
- dimension reduction
- subspace learning
- spiking neural networks
- semi supervised
- manifold structure
- input space
- nonlinear manifold
- scene classification
- manifold learning algorithm
- geodesic distance
- artificial neural networks
- euclidean space
- data points
- humanoid robot
- sparse representation
- embedding space
- locally linear embedding
- back propagation
- nonlinear manifold learning
- low dimensional manifolds
- linear subspace
- feature space
- pattern recognition
- feature extraction
- latent space
- vector space
- nearest neighbor
- knn