A distance-based point-reassignment heuristic for the k-hyperplane clustering problem.
Edoardo AmaldiStefano ConiglioPublished in: Eur. J. Oper. Res. (2013)
Keyphrases
- hyperplane
- data points
- high dimensional data sets
- normal vectors
- feature space
- outlier detection
- training samples
- linear classifiers
- euclidean distance
- principal components
- linear separability
- linearly separable
- input space
- support vector
- support vector machine
- unsupervised learning
- incremental learning algorithm
- dynamic programming
- svm classifier
- maximal margin
- classification procedure
- support vectors
- kernel function
- distance measure
- machine learning
- convex hull
- low dimensional
- nearest neighbor
- image classification
- nearest point
- data sets