Dimension reduction for objects composed of vector sets.
Marton SzemenyeiFerenc VajdaPublished in: Int. J. Appl. Math. Comput. Sci. (2017)
Keyphrases
- dimension reduction
- principal component analysis
- data mining and machine learning
- high dimensional
- low dimensional
- high dimensional problems
- manifold learning
- feature selection
- variable selection
- high dimensional data
- linear discriminant analysis
- dimension reduction methods
- dimensionality reduction
- partial least squares
- feature extraction
- discriminative information
- high dimensional data analysis
- singular value decomposition
- random projections
- sparse metric learning
- d objects
- high dimensionality
- manifold embedding
- cluster analysis
- vector space
- data warehouse
- supervised learning
- discriminant analysis
- feature subspace
- learning algorithm
- unsupervised learning