Latent Space Exploration Using Generative Kernel PCA.
David WinantJoachim SchreursJohan A. K. SuykensPublished in: BNAIC/BENELEARN (Selected Papers) (2019)
Keyphrases
- kernel pca
- latent space
- laplacian eigenmaps
- generative model
- dimensionality reduction
- gaussian process latent variable models
- feature space
- principal component analysis
- low dimensional
- lower dimensional
- latent variables
- manifold learning
- kernel methods
- kernel function
- spectral clustering
- kernel principal component analysis
- high dimensional
- probabilistic model
- unsupervised learning
- parameter space
- kernel matrix
- face recognition
- nonlinear dimensionality reduction
- high dimensional data
- bayesian framework
- prior knowledge
- feature extraction
- gaussian processes
- pattern recognition
- high dimensional spaces
- image segmentation
- semi supervised
- gaussian process
- matrix factorization
- dimension reduction
- linear discriminant analysis
- topic models
- latent dirichlet allocation
- support vector
- probabilistic latent semantic analysis
- machine learning
- em algorithm
- support vector machine