A dimensionality reduction approach for convolutional neural networks.
Laura MeneghettiNicola DemoGianluigi RozzaPublished in: Appl. Intell. (2023)
Keyphrases
- convolutional neural networks
- dimensionality reduction
- convolutional network
- low dimensional
- feature extraction
- high dimensional
- feature space
- high dimensional data
- feature selection
- principal components
- preprocessing step
- high dimensionality
- dimensionality reduction methods
- linear discriminant analysis
- principal component analysis
- data points
- pattern recognition and machine learning
- input space
- data representation
- nonlinear dimensionality reduction
- random projections
- manifold learning
- euclidean distance
- pattern recognition
- linear dimensionality reduction
- structure preserving
- lower dimensional
- locally linear embedding
- metric learning
- linear projection
- intrinsic dimensionality
- singular value decomposition
- sparse representation
- kernel pca
- least squares