SPTM-L3.5

A FRAMEWORK FOR BIPARTITE GRAPH STRUCTURE LEARNING THROUGH EIGENVECTOR PARTITIONING

Xintong Shi, Aimin Jiang, Rui Yang, Yibin Tang, Min Li, Hohai University, China; Yanping Zhu, Changzhou University, China
Session:
SPTM-L3: Graph Topology Inference and Learning Oral
Track:
Signal Processing Theory and Methods [TM]
Location:
Room 124+125
Presentation Time:
Tue, 5 May, 15:20 - 15:40
Presentation
Discussion
Resources
No resources available.
Session SPTM-L3
SPTM-L3.1: MULTIVIEW GRAPH LEARNING WITH CONSENSUS GRAPH
Abdullah Karaaslanli, Selin Aviyente, Michigan State University, United States of America
SPTM-L3.2: Cramér-Rao Bounds for Laplacian Matrix Estimation
Morad Halihal, Ben-Gurion University of the Negev, Israel; Tirza Routtenberg, H. Vincent Poor, Princeton University, Israel
SPTM-L3.3: LEARNING GRAPH FROM SMOOTH SIGNALS UNDER PARTIAL OBSERVATION: A ROBUSTNESS ANALYSIS
Hoang-Son Nguyen, Oregon State University, United States of America; Hoi-To Wai, The Chinese University of Hong Kong, Hong Kong
SPTM-L3.4: LEARNING PRODUCT GRAPHS FROM TWO-DIMENSIONAL STATIONARY SIGNALS
Andrei Buciulea, Universidad Rey Juan Carlos, Spain; Bishwadeep Das, Elvin Isufi, Delft University of Technology, Netherlands; Antonio García Marqués, Universidad Rey Juan Carlos, Spain
SPTM-L3.5: A FRAMEWORK FOR BIPARTITE GRAPH STRUCTURE LEARNING THROUGH EIGENVECTOR PARTITIONING
Xintong Shi, Aimin Jiang, Rui Yang, Yibin Tang, Min Li, Hohai University, China; Yanping Zhu, Changzhou University, China
SPTM-L3.6: LEARNING THE STRUCTURE OF CONNECTION GRAPHS
Leonardo Di Nino, Gabriele D'Acunto, Sergio Barbarossa, Paolo Di Lorenzo, Sapienza, Università di Roma, Italy
Contacts