Discriminative geodesic Gaussian process latent variable model for structure preserving dimension reduction in clustering and classification problems.
Mahdi HeidariMohammad Hossein MoattarPublished in: Neural Comput. Appl. (2019)
Keyphrases
- dimension reduction
- structure preserving
- dimensionality reduction
- low dimensional
- gaussian process latent variable models
- discriminative information
- unsupervised learning
- high dimensional data
- latent space
- high dimensional
- high dimensionality
- cluster analysis
- feature extraction
- principal component analysis
- manifold learning
- data points
- feature selection
- feature space
- k means
- clustering algorithm
- linear discriminant analysis
- singular value decomposition
- clustering method
- lower dimensional
- random projections
- pattern recognition
- distance metric
- semi supervised
- semi supervised learning
- spectral clustering
- data sets
- parameter space
- euclidean space
- decision trees
- probabilistic latent semantic analysis
- euclidean distance
- support vector machine
- data mining