Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data.
Luis O. Jimenez-RodriguezEmmanuel Arzuaga-CruzMiguel Velez-ReyesPublished in: IEEE Trans. Geosci. Remote. Sens. (2007)
Keyphrases
- high dimensional data
- high dimensionality
- dimensionality reduction
- dimension reduction
- low dimensional
- unsupervised learning
- nearest neighbor
- data sets
- high dimensional
- small sample size
- data points
- subspace clustering
- regression problems
- supervised learning
- data analysis
- high dimensions
- similarity search
- feature extraction
- lower dimensional
- linear discriminant analysis
- machine learning
- multivariate temporal data
- support vector machine svm
- decision trees
- pattern recognition
- high dimensional spaces
- feature space
- classification algorithm
- semi supervised
- support vector machine
- sparse representation
- text classification
- high dimensional datasets
- high dimensional data sets
- variable weighting
- class labels
- input data
- principal component analysis
- active learning
- dimensional data
- model selection
- nonlinear dimensionality reduction
- support vector
- training samples