Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives.
Andrzej CichockiAnh Huy PhanQibin ZhaoNamgil LeeIvan V. OseledetsMasashi SugiyamaDanilo P. MandicPublished in: Found. Trends Mach. Learn. (2017)
Keyphrases
- dimensionality reduction
- high dimensional
- high dimensional data
- data representation
- optimization algorithm
- long term
- principal component analysis
- optimization problems
- feature extraction
- low dimensional
- current issues
- subspace learning
- random projections
- high dimensionality
- real world
- data points
- higher order
- constrained optimization
- linear discriminant analysis
- small scale
- pattern recognition and machine learning
- social networks
- supervised dimensionality reduction
- manifold learning
- principal components
- multi objective
- pattern recognition
- singular value decomposition
- network structure
- high order
- dimensionality reduction methods
- linear transformation
- locally linear embedding
- sparse representation
- future internet