High Dimensional Bayesian Optimization via Supervised Dimension Reduction.
Miao ZhangHuiqi LiSteven W. SuPublished in: IJCAI (2019)
Keyphrases
- dimension reduction
- high dimensional
- low dimensional
- high dimensional problems
- high dimensionality
- high dimensional data
- feature space
- principal component analysis
- unsupervised learning
- variable selection
- feature selection
- manifold learning
- dimensionality reduction
- data mining and machine learning
- feature extraction
- random projections
- high dimensions
- partial least squares
- similarity search
- discriminative information
- singular value decomposition
- high dimensional data analysis
- learning algorithm
- linear discriminant analysis
- cluster analysis
- maximum likelihood
- supervised learning
- manifold embedding
- dimension reduction methods
- neural network
- text mining
- least squares
- nearest neighbor
- high dimensional feature space
- small sample size
- image processing
- intrinsic dimension