The impact of the norm on the k-Hyperplane Clustering problem: relaxations, restrictions, approximation factors, and exact formulations.
Stefano ConiglioPublished in: CTW (2011)
Keyphrases
- hyperplane
- data points
- support vector
- training samples
- input space
- euclidean norm
- feature space
- linear classifiers
- support vector machine
- incremental learning algorithm
- lower bound
- linearly separable
- maximal margin
- convex hull
- np hard
- objective function
- kernel function
- principal components
- high dimensional data
- svm classifier
- high dimensional
- principal direction
- linear separability
- text classification
- support vectors
- linear programming