Specific Emitter Identification with Principal Component Analysis (PCA) Dimensionality Reduction and Convolutional Neural Network.
Gianmarco BaldiniFausto BonavitacolaPublished in: SmartNets (2023)
Keyphrases
- principal component analysis
- dimensionality reduction
- convolutional neural network
- principal components
- low dimensional
- feature extraction
- feature space
- linear discriminant analysis
- independent component analysis
- kernel pca
- subspace learning
- dimensionality reduction methods
- lower dimensional
- singular value decomposition
- random projections
- discriminant analysis
- face recognition
- linear dimensionality reduction
- dimension reduction
- data representation
- input space
- pattern recognition
- linear projection
- covariance matrix
- high dimensional data
- principal components analysis
- face images
- principle component analysis
- kernel principal component analysis
- dimensional reduction
- nonlinear dimensionality reduction
- manifold learning
- high dimensionality
- pattern recognition and machine learning
- subspace methods
- multi class
- training procedure
- structure preserving
- tensor analysis
- high dimensional
- metric learning
- feature selection
- dimension reduction methods
- reduced dimensionality