Name
Title | ||
|---|---|---|
FIBER-SAMPLED STOCHASTIC MIRROR DESCENT FOR TENSOR DECOMPOSITION WITH beta-DIVERGENCE |
Abstract | ||
|---|---|---|
Canonical polyadic decomposition (CPD) has been a workhorse for multimodal data analytics. This work puts forth a stochastic algorithmic framework for CPD under beta-divergence, which is well-motivated in statistical learning-where the Euclidean distance is typically not preferred. Despite the existence of a series of prior works addressing this topic, pressing computational and theoretical challenges, e.g., scalability and convergence issues, still remain. In this paper, a unified stochastic mirror descent framework is developed for large-scale beta-divergence CPD. Our key contribution is the integrated design of a tensor fiber sampling strategy and a flexible stochastic Bregman divergence-based mirror descent iterative procedure, which significantly reduces the computation and memory cost per iteration for various beta. Leveraging the fiber sampling scheme and the multilinear algebraic structure of low-rank tensors, the proposed lightweight algorithm also ensures global convergence to a stationary point under mild conditions. Numerical results on synthetic and real data show that our framework attains significant computational saving compared with state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICASSP39728.2021.9413830 | 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) |
Keywords | DocType | Citations |
Tensor decomposition, beta-divergence, stochastic optimization, mirror descent method | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenqiang Pu | 1 | 48 | 7.37 |
| Ibrahim, Shahana | 2 | 0 | 1.69 |
| Xiao Fu | 3 | 100 | 17.78 |
| Mingyi Hong | 4 | 1533 | 91.29 |