Feature space approximation for kernel-based supervised learning.
Patrick GelßStefan KlusIngmar SchusterChristof SchüttePublished in: Knowl. Based Syst. (2021)
Keyphrases
- feature space
- supervised learning
- kernel methods
- training set
- kernel pca
- training samples
- support vector machine
- kernel function
- unsupervised learning
- learning tasks
- kernel trick
- active learning
- mean shift
- approximation algorithms
- learning problems
- high dimensional
- approximation error
- training data
- closed form
- feature extraction
- feature vectors
- data points
- statistical learning
- machine learning
- learning algorithm
- high dimensionality
- high dimensional feature space
- low dimensional
- semi supervised
- classification accuracy
- image retrieval
- hyperplane
- mercer kernel
- feature selection
- supervised machine learning
- image processing
- reinforcement learning
- riemannian manifolds
- support vector
- multiple kernel learning
- semi supervised learning
- knn
- dimensionality reduction
- visual words
- online learning
- image representation