A Unified Framework for Structured Graph Learning via Spectral
Constraints

A Unified Framework for Structured Graph Learning via Spectral Constraints

22 April 2019

José Vinícius de Miranda Cardoso

Daniel P. Palomar

Papers citing "A Unified Framework for Structured Graph Learning via Spectral Constraints"

12 / 12 papers shown

Title
Joint Signal Recovery and Graph Learning from Incomplete Time-Series Amirhossein Javaheri Arash Amini F. Marvasti Daniel P. Palomar 11 2 0 28 Dec 2023
A Unified Framework for Fair Spectral Clustering With Effective Graph Learning Xiang Zhang Qiao Wang 20 3 0 23 Nov 2023
Sparse Graphical Linear Dynamical Systems Émilie Chouzenoux Victor Elvira 17 7 0 06 Jul 2023
Free Lunch for Privacy Preserving Distributed Graph Learning Nimesh Agrawal Nikita Malik Sandeep Kumar 9 0 0 18 May 2023
Graph Learning Across Data Silos Xiang Zhang Qiao Wang 16 1 0 17 Jan 2023
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging Ali Hashemi Yijing Gao Chang Cai Sanjay Ghosh Klaus-Robert Muller S. Nagarajan Stefan Haufe 11 7 0 02 Nov 2021
Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistency E. Pavez 9 4 0 31 Oct 2021
Learning Sparse Graph with Minimax Concave Penalty under Gaussian Markov Random Fields Tatsuya Koyakumaru M. Yukawa Eduardo Pavez Antonio Ortega 19 8 0 17 Sep 2021
Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks H. Sahbi 3DH GNN 8 3 0 09 Apr 2021
Algorithms for Learning Graphs in Financial Markets José Vinícius de Miranda Cardoso Jiaxi Ying Daniel P. Palomar CML AIFin 35 17 0 31 Dec 2020
Learning Graph Laplacian with MCP Yangjing Zhang Kim-Chuan Toh Defeng Sun 12 8 0 22 Oct 2020
Structured Graph Learning Via Laplacian Spectral Constraints Sandeep Kumar Jiaxi Ying J. Cardoso Daniel P. Palomar 32 57 0 24 Sep 2019