Tue AM1.L2.3
Enhanced Tensor Rank Learning in Bayesian PARAFAC2 for Noisy Irregular Tensor Data
Zhongtao Chen, The University of Hong Kong, China; Lei Cheng, Zhejiang University, China; Yik-Chung Wu, The University of Hong Kong, China
Session:
Tue AM1.L2: Learning with Low-Rank Models Lecture
Track:
Special Sessions
Location:
Fennia 1
Presentation Time:
Tue, 5 Sep, 11:40 - 12:00 Finland Time (UTC +3)
Session Chair:
Xiao Fu, Oregon State University
Presentation
Discussion
Resources
No resources available.
Session Tue AM1.L2
Tue AM1.L2.1: Deep Symmetric Matrix Factorization
Pierre De Handschutter, Nicolas Gillis, UMONS, Belgium; Wivine Blekic, NYU Grossman School of Medicine, United States
Tue AM1.L2.2: Bilinear Hyperspectral Unmixing via Tensor Decomposition
Meng Ding, Southwest Jiaotong University, China; Xiao Fu, Oregon State University, China; Xi-Le Zhao, University of Electronic Science and Technology of China, China
Tue AM1.L2.3: Enhanced Tensor Rank Learning in Bayesian PARAFAC2 for Noisy Irregular Tensor Data
Zhongtao Chen, The University of Hong Kong, China; Lei Cheng, Zhejiang University, China; Yik-Chung Wu, The University of Hong Kong, China
Tue AM1.L2.4: OVERDETERMINED SYSTEMS OF POLYNOMIAL EQUATIONS: TENSOR-BASED SOLUTION AND APPLICATION
Raphaël Widdershoven, Nithin Govindarajan, Lieven De Lathauwer, KU Leuven, Belgium
Tue AM1.L2.5: Tensor-based two-layer decoupling of multivariate polynomial maps
Konstantin Usevich, CNRS, France; Yassine Zniyed, Univ. de Toulon, Aix Marseille Univ., France; Mariya Ishteva, KU Leuven, Belgium; Philippe Dreesen, Maastricht University, Netherlands; André L. F. de Almeida, Federal University of Ceara, Brazil
Tue AM1.L2.6: Fairness-aware Dimensionality Reduction
Oyku Deniz Kose, Yanning Shen, University of California Irvine, United States