Spike pattern recognition by supervised classification in low dimensional embedding space.
Evangelia I. ZacharakiIosif MporasKyriakos GarganisVasileios MegalooikonomouPublished in: Brain Informatics (2016)
Keyphrases
- supervised classification
- embedding space
- low dimensional
- pattern recognition
- dimensionality reduction
- unsupervised learning
- euclidean space
- manifold learning
- high dimensional
- graph embedding
- machine learning
- input space
- supervised learning
- data points
- high dimensional data
- nonlinear dimensionality reduction
- geometric structure
- image analysis
- principal component analysis
- neural network
- feature extraction
- dimension reduction
- image processing
- geodesic distance
- subspace learning
- computer vision
- euclidean distance
- locally linear embedding
- feature representation
- training set
- image segmentation
- vector space
- linear discriminant analysis
- feature space
- data representation
- dimensionality reduction methods
- data mining
- metric learning
- support vector
- reinforcement learning
- similarity measure