High dimensional exploration: A comparison of PCA, distance concentration, and classification performance in two fMRI datasets.
Joset A. EtzelTodd S. BraverPublished in: CIDM (2014)
Keyphrases
- high dimensional
- feature space
- dimensionality reduction
- functional magnetic resonance imaging
- dimension reduction
- principal component analysis
- benchmark datasets
- dimensionality reduction methods
- pattern recognition
- uci repository
- classification accuracy
- high dimensionality
- principle component analysis
- high dimensional datasets
- pattern classification
- feature extraction
- uci machine learning repository
- low dimensional
- support vector machine
- kernel principal component analysis
- classification method
- feature selection
- training dataset
- decision trees
- machine learning
- feature vectors
- training samples
- classification algorithm
- svm classifier
- image classification
- covariance matrix
- small sample size
- nearest neighbor
- human brain
- principal components analysis
- class labels
- training set
- data analysis
- support vector
- face recognition
- distance measurement
- event related
- active learning
- high dimensional feature space
- data points
- supervised learning
- linear discriminant analysis
- text classification