Combining the Best Linear Approximation and Dimension Reduction to Identify the Linear Blocks of Parallel Wiener Systems.
Maarten SchoukensChristian LyzellMartin EnqvistPublished in: ALCOSP (2013)
Keyphrases
- dimension reduction
- linear approximation
- feature extraction
- principal component analysis
- high dimensional
- high dimensional data
- feature selection
- partial least squares
- data sets
- singular value decomposition
- random projections
- manifold learning
- basis functions
- locality preserving projections
- discriminative information
- linear discriminant analysis
- unsupervised learning
- dimensionality reduction
- preprocessing
- neural network