A Counterexample to the Possibility of an Extension of the Eckart-Young Low-Rank Approximation Theorem for the Orthogonal Rank Tensor Decomposition.
Tamara G. KoldaPublished in: SIAM J. Matrix Anal. Appl. (2003)
Keyphrases
- low rank approximation
- tensor decomposition
- low rank
- singular value decomposition
- subspace learning
- data representation
- linear combination
- matrix factorization
- missing data
- kernel matrix
- convex optimization
- high order
- low rank matrix
- spectral clustering
- matrix completion
- nonnegative matrix factorization
- tensor factorization
- high dimensional data
- auxiliary information
- semi supervised
- latent semantic indexing
- pairwise
- singular values
- iterative algorithms
- visual data
- least squares
- pattern recognition
- data sets
- information retrieval