SPTM-P3: Sparse and Low-dimensional Signal Recovery, Matrices, and Tensors
Poster
Tue, 5 May, 14:00 - 16:00
Location: Poster Area 41
Session Type: Poster
Session Co-Chairs: Eleftherios Kofidis, University of Piraeus and Xiao Fu, Oregon State University
Track: Signal Processing Theory and Methods [TM]
Click the to view the manuscript on IEEE Xplore Open Preview
 

SPTM-P3.1: SPARSE BAYESIAN LEARNING WITH SIMPLE AND INTERPRETABLE DNNS EXPLOITING DATA-DRIVEN PRIORS
Hitesh Khunti, Bhaskar Rao, University of California, San Diego, United States of America
 

SPTM-P3.2: COMPRESSIVE RECOVERY OF SIGNALS DEFINED ON PERTURBED GRAPHS
Sabyasachi Ghosh, Walmart Global Technology, India; Ajit Rajwade, IIT Bombay, India
 

SPTM-P3.3: Fast and Robust Triple Tensor Decomposition With Data Corruption
Quy Dang Nguyen, Minh Nhat Do, Thanh Trung Le, Linh Trung Nguyen, VNU University of Engineering and Technology, Vietnam National University, Hanoi, Viet Nam; Karim Abed-Meraim, University of Orleans, France
 

SPTM-P3.4: ROTATIONALLY-INVARIANT AMP FOR COMPRESSED SENSING WITH MULTIPLE MEASUREMENT VECTORS
Shengsong Luo, Fudan University, China; Songbin Liu, Columbia University, United States of America; Junjie Ma, Chinese Academy of Sciences, China; Chongbin Xu, Xin Wang, Fudan University, China
 

SPTM-P3.5: ON RANDOM POOLING OF LARGE-SCALE SCREENING WITH EXTREMELY SPARSE INFECTIONS
Jianing Li, Li Chai, Zhejiang University, China
 

SPTM-P3.6: A PSEUDOINVERSE-BASED MOMENTUM FISTA FOR SPARSE SIGNAL RECOVERY
Mahendra Kumar, Ramunaidu Randhi, Indian Institute of Petroleum and Energy, India
 

SPTM-P3.7: Large System Analysis of SURE based Hyper- parameter Optimizing in Sparse Bayesian Learning
Fangqing Xiao, Jichao Chen, Dirk Slock, EURECOM, France
 

SPTM-P3.8: SPARSE RECOVERY USING TIGHT FRAMES AND MINIMAX CONCAVE PENALTY
Kartheek Kumar Reddy Nareddy, German Aerospace Centre (DLR), Germany; Abhishek Shreekant Bhandiwad, Abijith Jagannath Kamath, Chandra Sekhar Seelamantula, Indian Institute of Science, India
 

SPTM-P3.9: RECOVERING COMPRESSED TENSORS USING DEEP FACTORIZATION MODELS
Precious Nwaorgu, Shahana Ibrahim, Chinwendu Enyioha, University of Central Florida, United States of America

SPTM-P3.10: Low-Rank Covariance Matrix Recovery From Rank-One Measurements: An Analytical Solution
Peilan Wang, Binyao Ma, Jun Fang, Bin Wang, University of Electronic and Science of Technology, China; Geert Leus, Delft University of Technology, Netherlands

SPTM-P3.11: SUFFICIENT CONDITIONS FOR CONVERGENCE OF RHT AND RHTP ALGORITHMS BASED ON RIC OF ORDER 2S
Sk Md Atique Anwar, Pradyumna Pradhan, Ramunaidu Randhi, Indian Institute of Petroleum and Energy, Visakhapatnam, India; Pradip Sasmal, Indian Institute of Technology, Jodhpur, Rajasthan 342030, India, India

SPTM-P3.12: Shift- and stretch-invariant non-negative matrix factorization with an application to brain tissue delineation in emission tomography data
Anders Olsen, Miriam Navarro, Claus Svarer, Neurobiology research unit, Copenhagen university hospital Rigshospitalet, Denmark; Jesper Hinrich, Morten Mørup, Technical University of Denmark, Denmark; Gitte Knudsen, Neurobiology research unit, Copenhagen university hospital Rigshospitalet, Denmark

SPTM-P3.13: RADIO MAP ESTIMATION VIA LATENT DOMAIN PLUG-AND-PLAY DENOISING
Le Xu, Oregon State University, United States of America; Lei Cheng, Zhejiang University, China; Junting Chen, Wenqiang Pu, The Chinese University of Hong Kong, China; Xiao Fu, Oregon State University, United States of America
 

SPTM-P3.14: KERNEL REGRESSION VIA TENSOR TRAINS WITH HADAMARD OVERPARAMETRIZATION AND IMPUTATION OF DYNAMIC GRAPH EDGE FLOWS
Duc Thien Nguyen, Konstantinos Slavakis, Institute of Science Tokyo, Japan; Eleftherios Kofidis, University of Piraeus, Greece; Dimitris Pados, Florida Atlantic University, United States of America
 

SPTM-P3.15: FAST AND ACCURATE TEMPORAL SUPER-RESOLUTION VIA RESIDUAL-AWARE COUPLED TENSOR FACTORIZATION
Nam Kyu Kang, Yong-chan Park, U Kang, Seoul National University, Korea, Republic of
 

SPTM-P3.16: LARGE-SYSTEM FIXED-POINT LAW AND DETERMINISTIC CLOSURE FOR SPARSE BAYESIAN LEARNING
Fangqing Xiao, Dirk Slock, Eurecom, France