MLSP-19.6
A MULTI-RESOLUTION LOW-RANK TENSOR DECOMPOSITION
Sergio Rozada Doval, Antonio G. Marques, King Juan Carlos University, Spain
Session:
Tensor-based Signal Processing
Track:
Machine Learning for Signal Processing
Location:
Gather Area H
Presentation Time:
Mon, 9 May, 22:00 - 22:45 China Time (UTC +8)
Mon, 9 May, 14:00 - 14:45 UTC
Mon, 9 May, 14:00 - 14:45 UTC
Session Chair:
Shuchin Aeron, Tufts University
Session MLSP-19
MLSP-19.1: ON THE RELAXATION OF ORTHOGONAL TENSOR RANK AND ITS NONCONVEX RIEMANNIAN OPTIMIZATION FOR TENSOR COMPLETION
Keisuke Ozawa, DENSO IT Laboratory, Japan
MLSP-19.2: Robust High-Order Tensor Recovery via Nonconvex Low-Rank Approximation
Wenjin Qin, Jianjun Wang, Southwest University, School of Mathematic and Statistics, China; Hailin Wang, Xi’an Jiaotong University, School of Mathematics and Statistics, China; Weijun Ma, Ningxia University, School of Information Engineering, China
MLSP-19.3: VARIATIONAL BAYESIAN TENSOR NETWORKS WITH STRUCTURED POSTERIORS
Kriton Konstantinidis, Yao Xu, Danilo Mandic, IMPERIAL COLLEGE LONDON, United Kingdom of Great Britain and Northern Ireland; Qibin Zhao, RIKEN CENTER FOR ADVANCED INTELLIGENCE PROJECT, Japan
MLSP-19.4: LOW-RANK PHASE RETRIEVAL WITH STRUCTURED TENSOR MODELS
Soo Min Kwon, Xin Li, Anand Sarwate, Rutgers, The State University of New Jersey, United States of America
MLSP-19.5: HOQRI: Higher-order QR Iteration for Scalable Tucker Decomposition
Yuchen Sun, Kejun Huang, University of Florida, United States of America
MLSP-19.6: A MULTI-RESOLUTION LOW-RANK TENSOR DECOMPOSITION
Sergio Rozada Doval, Antonio G. Marques, King Juan Carlos University, Spain