Dimension reduction and redundancy removal through successive Schmidt decompositions.
Ammar DaskinRishabh GuptaSabre KaisPublished in: CoRR (2023)
Keyphrases
- dimension reduction
- singular value decomposition
- principal component analysis
- feature extraction
- high dimensionality
- high dimensional problems
- variable selection
- high dimensional
- high dimensional data
- random projections
- low dimensional
- discriminative information
- dimensionality reduction
- linear discriminant analysis
- data mining and machine learning
- feature selection
- unsupervised learning
- partial least squares
- feature space
- manifold learning
- preprocessing
- cluster analysis
- sparse metric learning
- database
- real world
- data sets
- image data
- training set
- image processing
- feature subspace
- intrinsic dimension
- manifold embedding
- high dimensional data analysis