The Role of Multiple, Linear-Projection Based Visualization Techniques in RBF-Based Classification of High Dimensional Data.
Adrian K. AgoginoJoydeep GhoshStavros J. PerantonisVassilis VirvilisSergios PetridisPaulo J. G. LisboaPublished in: IJCNN (3) (2000)
Keyphrases
- high dimensional data
- high dimensionality
- dimension reduction
- data analysis
- low dimensional
- dimensionality reduction
- nearest neighbor
- high dimensional
- subspace clustering
- data sets
- small sample size
- data points
- support vector machine
- high dimensions
- feature space
- high dimensional feature spaces
- input space
- support vector machine svm
- support vector
- pattern recognition
- similarity search
- regression problems
- feature selection
- multivariate temporal data
- high dimensional data sets
- high dimensional spaces
- radial basis function
- clustering high dimensional data
- high dimensional datasets
- subspace learning
- feature extraction
- input data
- knn
- feature vectors
- manifold learning
- decision trees
- dimensional data
- text classification
- text data
- clustering algorithm
- training data
- linear discriminant analysis
- classification algorithm
- dimensionality reduction methods
- class labels
- sparse representation
- nonlinear dimensionality reduction
- machine learning
- database
- real world
- underlying manifold
- feature set