Optimizing the kernel in the empirical feature space.
Huilin XiongM. N. S. SwamyM. Omair AhmadPublished in: IEEE Trans. Neural Networks (2005)
Keyphrases
- feature space
- kernel function
- kernel methods
- input space
- high dimensional feature space
- kernel matrix
- principal component analysis
- kernel space
- feature vectors
- high dimensional
- dot product
- training samples
- feature selection
- data points
- kernel trick
- optimal kernel
- feature extraction
- support vector machine
- dimensionality reduction
- low dimensional
- high dimensionality
- machine learning
- linear discriminant analysis
- hyperplane
- mean shift
- classification accuracy
- mercer kernels
- mercer kernel
- dissimilarity measure
- support vector
- training set
- information theoretic
- linear separability
- positive definite
- high dimension
- kernel pca
- support vectors
- theoretical analysis
- image representation
- kernel principal component analysis
- linearly separable
- input data
- class separability
- multiple kernel
- kernel learning
- linear combination
- dimension reduction
- generalized discriminant analysis
- data sets