A variant of sparse partial least squares for variable selection and data exploration.
MeganOlson HuntLisa A. WeissfeldRobert BoudreauHoward AizensteinAnne B. NewmanEleanor M. SimonsickDane R. Van DomelenFridtjof ThomasKristine YaffeCaterina RosanoPublished in: Frontiers Neuroinformatics (2014)
Keyphrases
- variable selection
- partial least squares
- data exploration
- dimension reduction
- ls svm
- high dimensional
- canonical correlation analysis
- group lasso
- input variables
- data visualization
- cross validation
- data analysis
- principal component analysis
- data management
- low dimensional
- feature extraction
- databases
- high dimensional data
- linear discriminant analysis
- discriminant analysis
- model selection
- knowledge discovery
- unsupervised learning
- high dimensionality
- singular value decomposition
- feature space
- sparse representation
- nearest neighbor
- data points
- information visualization
- pattern recognition
- data mining techniques
- fuzzy logic
- real world