A modular eigen subspace scheme for high-dimensional data classification.
Yang-Lang ChangChin-Chuan HanFan-Di JouKuo-Chin FanKun-Shan ChenJeng-Horng ChangPublished in: Future Gener. Comput. Syst. (2004)
Keyphrases
- high dimensional data
- high dimensionality
- low dimensional
- dimension reduction
- subspace clustering
- dimensionality reduction
- lower dimensional
- high dimensional
- nearest neighbor
- small sample size
- clustering high dimensional data
- high dimensions
- regression problems
- data sets
- data analysis
- principal component analysis
- data points
- similarity search
- feature space
- input space
- linear discriminant analysis
- manifold learning
- low rank
- pattern recognition
- subspace learning
- original data
- high dimensional feature spaces
- sparse representation
- support vector machine svm
- feature extraction
- high dimensional data sets
- machine learning
- nonlinear dimensionality reduction
- support vector machine
- dimensionality reduction methods
- training set
- class labels
- high dimensional spaces
- support vector
- decision trees
- high dimensional datasets
- dimensional data
- random projections
- feature vectors
- text data
- text classification
- training samples
- multivariate temporal data
- subspace clusters
- discriminant analysis
- feature selection
- data mining