Autoencoders as an alternative approach to principal component analysis for dimensionality reduction. An application on simulated data from psychometric models.
Monica CasellaPasquale DolceMichela PonticorvoDavide MaroccoPublished in: PSYCHOBIT (2021)
Keyphrases
- dimensionality reduction
- principal component analysis
- principal components
- high dimensionality
- statistical models
- independent component analysis
- probabilistic model
- low dimensional
- random projections
- lower dimensional
- singular value decomposition
- covariance matrix
- dimensionality reduction methods
- high dimensional
- feature extraction
- nonlinear dimensionality reduction
- neural classifier
- neural network
- manifold learning
- sparse representation
- graphical models
- denoising
- face recognition
- feature selection