Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions.
Andrzej CichockiNamgil LeeIvan V. OseledetsAnh Huy PhanQibin ZhaoDanilo P. MandicPublished in: Found. Trends Mach. Learn. (2016)
Keyphrases
- singular values
- singular value decomposition
- low rank
- dimensionality reduction
- trace norm
- high dimensional data
- frobenius norm
- matrix completion
- tensor decomposition
- high order
- nuclear norm
- matrix decomposition
- principal component analysis
- low rank matrix
- high dimensional
- rank minimization
- higher order
- manifold learning
- low dimensional
- kernel matrix
- data points
- norm minimization
- subspace learning
- feature selection
- low rank matrices
- convex optimization
- data matrix
- matrix factorization
- low rank approximation
- linear combination
- pattern recognition
- pairwise
- robust principal component analysis
- neural network
- random projections
- data representation
- euclidean distance
- missing data
- feature extraction
- functional modules
- minimization problems
- kernel learning
- image denoising
- semi supervised
- learning algorithm