Parsimonious Mahalanobis kernel for the classification of high dimensional data.
Mathieu FauvelJocelyn ChanussotJón Atli BenediktssonAlberto VillaPublished in: Pattern Recognit. (2013)
Keyphrases
- high dimensional data
- high dimensionality
- dimension reduction
- high dimensional
- input space
- dimensionality reduction
- feature space
- low dimensional
- nearest neighbor
- subspace clustering
- support vector
- regression problems
- pattern recognition
- data points
- high dimensions
- data sets
- nonlinear dimensionality reduction
- small sample size
- kernel function
- high dimensional feature spaces
- clustering high dimensional data
- decision trees
- similarity search
- multivariate temporal data
- data analysis
- text classification
- high dimensional datasets
- linear discriminant analysis
- machine learning
- lower dimensional
- dimensional data
- high dimensional spaces
- kernel machines
- high dimensional feature space
- low rank
- manifold learning
- class labels
- support vector machine svm
- training samples
- image classification
- support vector machine
- feature vectors
- metric learning
- hyperplane
- subspace learning
- microarray
- dimensionality reduction methods
- distance function
- training set
- image processing