Randomized algorithms for low-rank tensor decompositions in the Tucker format.
Rachel MinsterArvind K. SaibabaMisha E. KilmerPublished in: CoRR (2019)
Keyphrases
- randomized algorithms
- low rank
- tensor decomposition
- singular value decomposition
- tensor factorization
- matrix factorization
- high order
- matrix decomposition
- lower bound
- linear combination
- matrix completion
- convex optimization
- missing data
- trace norm
- low rank matrix
- approximation algorithms
- frobenius norm
- practical problems
- rank minimization
- semi supervised
- worst case
- collaborative filtering
- singular values
- randomized algorithm
- high dimensional data
- data representation
- least squares
- data matrix
- auxiliary information
- higher order
- dimensionality reduction
- negative matrix factorization
- factorization method
- principal component analysis
- learning algorithm
- dynamic programming
- special case
- active learning