PCA-AE: Principal Component Analysis Autoencoder for Organising the Latent Space of Generative Networks.
Chi-Hieu PhamSaïd LadjalAlasdair NewsonPublished in: J. Math. Imaging Vis. (2022)
Keyphrases
- principal component analysis
- latent space
- low dimensional
- lower dimensional
- dimensionality reduction
- gaussian process latent variable models
- generative model
- principal components
- dimension reduction
- feature space
- covariance matrix
- linear discriminant analysis
- feature extraction
- unsupervised learning
- manifold learning
- random projections
- gaussian process
- latent variables
- high dimensional data
- face images
- face recognition
- singular value decomposition
- high dimensional
- negative matrix factorization
- high dimensional spaces
- probabilistic model
- high dimensionality
- machine learning
- higher dimensional
- probabilistic latent semantic analysis
- kernel principal component analysis
- gaussian mixture model