Self-organizing maps as a dimension reduction approach for spatial global sensitivity analysis visualization.
Seda Salap-AyçaPublished in: Trans. GIS (2022)
Keyphrases
- self organizing maps
- sensitivity analysis
- dimension reduction
- unsupervised learning
- generative topographic mapping
- principal component analysis
- high dimensional
- neural network
- high dimensional data
- feature extraction
- high dimensional problems
- managerial insights
- linear discriminant analysis
- low dimensional
- visualization methods
- singular value decomposition
- competitive learning
- input data
- feature selection
- supervised learning
- dimensionality reduction
- cluster analysis
- neural gas
- k means
- high dimensionality
- manifold learning
- feature space
- preprocessing
- data mining techniques
- computer vision