Sufficient dimension reduction in regressions through cumulative Hessian directions.
Li-Mei ZhangLi-Ping ZhuLi-Xing ZhuPublished in: Stat. Comput. (2011)
Keyphrases
- dimension reduction
- feature extraction
- principal component analysis
- high dimensional
- data mining and machine learning
- variable selection
- feature selection
- high dimensional problems
- manifold learning
- high dimensional data
- random projections
- low dimensional
- singular value decomposition
- dimensionality reduction
- cluster analysis
- high dimensionality
- unsupervised learning
- partial least squares
- high dimensional data analysis
- dimension reduction methods
- feature space
- linear discriminant analysis
- discriminative information
- data sets
- data points
- preprocessing
- linear model
- data analysis
- pattern recognition
- feature subspace
- training data
- manifold embedding
- least squares