A Principal Component Analysis (PCA)-based framework for automated variable selection in geodemographic classification.
Yunzhe LiuAlex SingletonDaniel Arribas-BelPublished in: Geo spatial Inf. Sci. (2019)
Keyphrases
- variable selection
- principal component analysis
- dimension reduction
- cross validation
- principal components
- linear models
- input variables
- feature extraction
- machine learning
- feature space
- pattern recognition
- model selection
- support vector machine
- support vector
- feature selection
- independent component analysis
- covariance matrix
- artificial neural networks
- low dimensional
- dimensionality reduction
- training set
- decision trees
- principle component analysis
- data sets
- high dimensionality
- singular value decomposition
- image classification
- classification accuracy
- face recognition
- image processing
- data mining