Dimension reduction in radio maps based on the supervised kernel principal component analysis.
Bing JiaBaoqi HuangHepeng GaoWuyungerile LiPublished in: Soft Comput. (2018)
Keyphrases
- dimension reduction
- kernel principal component analysis
- principal component analysis
- feature extraction
- linear discriminant analysis
- high dimensional
- feature space
- kernel pca
- discriminant analysis
- principal components
- preprocessing
- dimensionality reduction
- unsupervised learning
- feature selection
- low dimensional
- face recognition
- random projections
- feature vectors
- learning algorithm
- manifold learning
- singular value decomposition
- semi supervised
- variable selection
- kernel function
- high dimensional data
- high dimensionality
- input space
- covariance matrix
- kernel methods
- cluster analysis
- pattern recognition
- kernel matrix
- least squares
- supervised learning
- lower dimensional
- face images
- image processing
- support vector machine
- machine learning