Sleep Stages Classification from Electroencephalographic Signals Based on Unsupervised Feature Space Clustering.
Iosif MporasAnastasia EfstathiouVasileios MegalooikonomouPublished in: BIH (2015)
Keyphrases
- eeg signals
- feature space
- unsupervised learning
- sleep stage
- high dimensionality
- unsupervised classification
- classification accuracy
- supervised classification
- supervised learning
- unsupervised clustering
- feature vectors
- feature selection
- eeg data
- signal processing
- training set
- unsupervised feature selection
- training samples
- clustering method
- unsupervised manner
- pattern recognition
- dissimilarity measure
- class separability
- support vector machine
- feature extraction
- clustering analysis
- brain computer interface
- data clustering
- k means
- partially supervised
- data points
- cluster analysis
- dimension reduction
- text classification
- image classification
- dimensionality reduction
- high dimension
- information bottleneck
- cluster validation
- acoustic signals
- clustering algorithm
- sleep apnea
- semi supervised
- machine learning
- kernel principal component analysis
- pairwise
- input space
- spectral clustering
- high dimensional data
- kernel function
- image representation
- model selection
- feature set
- principal component analysis