A Low-Dimensional Principal Manifold as the "Attractor Backbone" of a Chaotic Beam System.
Erik M. BolltJoseph D. SkufcaPublished in: Int. J. Bifurc. Chaos (2015)
Keyphrases
- input space
- low dimensional
- cellular automata
- phase space
- high dimensional
- dimensionality reduction
- manifold learning
- high dimensional data
- data points
- chaotic dynamics
- lower dimensional
- dynamical systems
- feature space
- euclidean space
- manifold structure
- manifold learning algorithm
- principal component analysis
- nonlinear dimensionality reduction
- chaotic neural network
- linear subspace
- dimension reduction
- lyapunov exponents
- subspace learning
- fixed point
- nonlinear manifold
- multi object
- locally linear embedding
- vector space
- embedding space
- diffusion maps
- graph embedding
- multidimensional scaling
- reinforced concrete
- cross section
- low dimensional manifolds
- laplacian eigenmaps
- nonlinear dynamics
- hopfield neural network
- dynamic behavior
- data sets
- feature selection