Efficient algorithms for Tucker decomposition via approximate matrix multiplication

发布时间：2026-06-05 作者：浏览次数：

Speaker:	车茂林	DateTime:	2026年6月5日（周五）下午15:30-16:30
Brief Introduction to Speaker: 车茂林，贵州大学		Place:	国交2号楼315会议室
Abstract:In this report, we develop fast and efficient algorithms for computing Tucker decomposition with a given multilinear rank. By combining random projection and the power scheme, we propose two efficient randomized versions for the truncated high-order singular value decomposition (T-HOSVD) and the sequentially T-HOSVD (ST-HOSVD), which are two common algorithms for approximating Tucker decomposition. To reduce the complexities of these two algorithms, fast and efficient algorithms are designed by combining two algorithms and approximate matrix multiplication. The theoretical results are also achieved based on the bounds of singular values of standard Gaussian matrices and the theoretical results for approximate matrix multiplication. Finally, the efficiency of these algorithms are illustrated via some test tensors from synthetic and real datasets.