An Efficient Randomized Algorithm for Computing the Approximate Tucker Decomposition
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 32 |
| Journal / Publication | Journal of Scientific Computing |
| Volume | 88 |
| Issue number | 2 |
| Online published | 17 Jun 2021 |
| Publication status | Published - Aug 2021 |
Link(s)
Abstract
By combining the thin QR decomposition and the subsampled randomized Fourier transform (SRFT), we obtain an efficient randomized algorithm for computing the approximate Tucker decomposition with a given target multilinear rank. We also combine this randomized algorithm with the power iteration technique to improve the efficiency of the algorithm. By using the results about the singular values of the product of orthonormal matrices with the Kronecker product of SRFT matrices, we obtain the error bounds of these two algorithms. Finally, the efficiency of these algorithms is illustrated by several numerical examples.
Research Area(s)
- Approximate Tucker decomposition, Dimension reduction maps, Power iteration technique, Random projection, Randomized algorithms, Subsampled randomized Fourier transform, Thin QR decomposition
Citation Format(s)
An Efficient Randomized Algorithm for Computing the Approximate Tucker Decomposition. / Che, Maolin; Wei, Yimin; Yan, Hong.
In: Journal of Scientific Computing, Vol. 88, No. 2, 32, 08.2021.
In: Journal of Scientific Computing, Vol. 88, No. 2, 32, 08.2021.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
