Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format.
Rachel MinsterArvind K. SaibabaMisha E. KilmerPublished in: SIAM J. Math. Data Sci. (2020)
Keyphrases
- randomized algorithms
- low rank
- tensor decomposition
- singular value decomposition
- tensor factorization
- matrix factorization
- matrix decomposition
- high order
- approximation algorithms
- linear combination
- missing data
- trace norm
- convex optimization
- lower bound
- low rank matrix
- matrix completion
- frobenius norm
- singular values
- rank minimization
- semi supervised
- high dimensional data
- practical problems
- data representation
- dimensionality reduction
- negative matrix factorization
- randomized algorithm
- data matrix
- factorization method
- worst case
- auxiliary information
- principal component analysis
- markov random field
- least squares
- machine learning