Latent Space Exploration Using Generative Kernel PCA.
David WinantJoachim SchreursJohan A. K. SuykensPublished in: BNAIC/BENELEARN (2019)
Keyphrases
- kernel pca
- latent space
- laplacian eigenmaps
- generative model
- dimensionality reduction
- feature space
- gaussian process latent variable models
- principal component analysis
- low dimensional
- latent variables
- manifold learning
- lower dimensional
- kernel principal component analysis
- kernel methods
- gaussian process
- unsupervised learning
- kernel function
- high dimensional
- feature extraction
- spectral clustering
- probabilistic model
- pattern recognition
- linear discriminant analysis
- gaussian processes
- semi supervised
- high dimensional data
- face recognition
- parameter space
- image classification
- kernel matrix
- matrix factorization
- bayesian framework
- nonlinear dimensionality reduction
- singular value decomposition
- feature selection
- computer vision
- probabilistic latent semantic analysis
- support vector
- high dimensional spaces
- random projections
- dimension reduction
- learning algorithm
- training samples
- em algorithm