Tensor decomposition via joint matrix schur decomposition. / Colombo, Nicolo; Vlassis, Nikos.
ICML'16 : Proceedings of the 33rd International Conference on International Conference on Machine Learning. ed. / Kilian Q. Weinberger; Maria Florina Balcan. ACM, 2016. p. 2820-2828 (33rd International Conference on Machine Learning, ICML 2016; Vol. 48).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Final published version
We describe an approach to tensor decomposition that involves extracting a set of observable matrices from the tensor and applying an approximate joint Schur decomposition on those matrices, and wc establish the corresponding first-order perturbation bounds. We develop a novel iterative Gauss-Newton algorithm for joint matrix Schur decomposition, which minimizes a nonconvex objective over the manifold of orthogonal matrices, and which is guaranteed to con-verge to a global optimum under certain conditions. We empirically demonstrate that our algorithm is faster and at least as accurate and robust than state-of-the-art algorithms for this problem.
Original language | English |
---|---|
Title of host publication | ICML'16 |
Subtitle of host publication | Proceedings of the 33rd International Conference on International Conference on Machine Learning |
Editors | Kilian Q. Weinberger, Maria Florina Balcan |
Publisher | ACM |
Pages | 2820-2828 |
Number of pages | 9 |
ISBN (Electronic) | 9781510829008 |
DOIs | |
Publication status | Published - 19 Jun 2016 |
Event | 33rd International Conference on Machine Learning, ICML 2016 - New York City, United States Duration: 19 Jun 2016 → 24 Jun 2016 |
Name | 33rd International Conference on Machine Learning, ICML 2016 |
---|---|
Volume | 48 |
Conference | 33rd International Conference on Machine Learning, ICML 2016 |
---|---|
Country/Territory | United States |
City | New York City |
Period | 19/06/16 → 24/06/16 |
ID: 34305272