Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets.
Parisa Abdolrahim PoorheraviVincent C. GaudetPublished in: ISMVL (2021)
Keyphrases
- subspace learning
- dimensionality reduction
- principal component analysis
- high dimensional data
- low dimensional
- manifold learning
- unsupervised learning
- data representation
- high dimensional
- high dimensionality
- feature space
- data sets
- pattern recognition
- singular value decomposition
- data analysis
- data points
- feature extraction
- graph embedding
- locality preserving projections
- maximum margin criterion
- sparse representation
- feature selection
- linear discriminant analysis
- dimensionality reduction methods
- linear dimensionality reduction
- dimension reduction
- metric learning
- machine learning
- model selection
- sparse coding
- signal processing
- pairwise
- similarity measure
- face recognition
- fisher criterion