Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets.
Parisa Abdolrahim PoorheraviVincent C. GaudetPublished in: CoRR (2021)
Keyphrases
- subspace learning
- dimensionality reduction
- principal component analysis
- high dimensional
- unsupervised learning
- manifold learning
- low dimensional
- high dimensional data
- data representation
- data points
- data sets
- pattern recognition
- dimensionality reduction methods
- feature space
- high dimensionality
- singular value decomposition
- linear discriminant analysis
- fisher criterion
- feature selection
- subspace learning algorithm
- linear dimensionality reduction
- sparse representation
- maximum margin criterion
- feature extraction
- denoising
- graph embedding
- locality preserving projections
- neural network
- dimension reduction
- relational databases
- data analysis
- multiscale