Using sliced mean variance-covariance inverse regression for classification and dimension reduction.
Charles D. LindseySimon J. SheatherJoseph W. McKeanPublished in: Comput. Stat. (2014)
Keyphrases
- dimension reduction
- partial least squares
- feature extraction
- high dimensional
- low dimensional
- principal component analysis
- high dimensional data
- discriminative information
- feature space
- data mining and machine learning
- feature selection
- dimensionality reduction
- random projections
- high dimensionality
- variable selection
- singular value decomposition
- high dimensional problems
- model selection
- unsupervised learning
- pattern recognition
- manifold learning
- high dimensional data analysis
- cluster analysis
- preprocessing
- linear discriminant analysis
- computer vision
- dimension reduction methods
- support vector machine svm
- supervised learning
- image processing
- data sets
- support vector machine
- support vector
- image sequences
- machine learning