An Information-theoretic approach to dimensionality reduction in data science.
Sambriddhi MainaliMax H. GarzonDeepak VenugopalKalidas JanaChing-Chi YangNirman KumarDale BowmanLih-Yuan DengPublished in: Int. J. Data Sci. Anal. (2021)
Keyphrases
- data science
- dimensionality reduction
- big data
- statistical learning
- machine learning
- principal component analysis
- pattern recognition
- manifold learning
- data representation
- high dimensional
- feature extraction
- high dimensionality
- high dimensional data
- pattern recognition and machine learning
- unsupervised learning
- feature selection
- low dimensional
- linear discriminant analysis
- dimensionality reduction methods
- data points
- singular value decomposition
- feature space
- data analysis
- principal components
- structure preserving
- information systems
- dimension reduction
- data warehouse
- decision trees
- information theory
- data warehousing
- sparse representation
- kernel pca
- active learning
- nonlinear dimensionality reduction
- social media
- neural network
- database