Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1.
Andrzej CichockiNamgil LeeIvan V. OseledetsAnh Huy PhanQibin ZhaoDanilo P. MandicPublished in: CoRR (2016)
Keyphrases
- low rank
- dimensionality reduction
- high dimensional data
- singular value decomposition
- optimization problems
- trace norm
- high order
- high dimensional
- tensor decomposition
- evolutionary algorithm
- high dimensionality
- low rank matrix
- principal component analysis
- rank minimization
- linear combination
- low dimensional
- frobenius norm
- matrix factorization
- kernel matrix
- convex optimization
- missing data
- manifold learning
- matrix completion
- data representation
- pattern recognition
- matrix decomposition
- subspace learning
- feature selection
- data points
- feature space
- random projections
- singular values
- semi supervised
- cost function
- objective function
- minimization problems
- feature extraction
- data matrix
- data sets
- low rank matrices
- kernel learning
- biological networks
- sparse representation
- nearest neighbor
- metric learning
- multi task
- optimization methods
- complex networks
- higher order
- machine learning