Dimension reduction in recurrent networks by canonicalization.
Lyudmila GrigoryevaJuan-Pablo OrtegaPublished in: CoRR (2020)
Keyphrases
- dimension reduction
- recurrent networks
- recurrent neural networks
- feed forward
- principal component analysis
- high dimensional
- biologically inspired
- singular value decomposition
- feature extraction
- variable selection
- neural network
- high dimensional data
- high dimensional problems
- linear discriminant analysis
- random projections
- high dimensionality
- feature selection
- low dimensional
- discriminative information
- partial least squares
- cluster analysis
- manifold learning
- high dimensional data analysis
- dimension reduction methods
- data mining and machine learning
- unsupervised learning
- dimensionality reduction
- feature space
- decision trees
- support vector
- preprocessing
- artificial neural networks
- back propagation
- multi modal
- data sets
- manifold embedding
- data points