Wei Chang

Orcid: 0000-0001-6286-5462

Affiliations:
  • Xi'an Modern Control Technology Research Institute, China
  • Northwestern Polytechnical University, School of Artificial Intelligence, Optics and Electronics, iOPEN, Xi'an, China (PhD 2023)


According to our database1, Wei Chang authored at least 13 papers between 2019 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Fast Semi-Supervised Learning on Large Graphs: An Improved Green-Function Method.
IEEE Trans. Pattern Anal. Mach. Intell., March, 2025

2024
Tensorized and Compressed Multi-View Subspace Clustering via Structured Constraint.
IEEE Trans. Pattern Anal. Mach. Intell., December, 2024

2023
Multitask Learning for Classification Problem via New Tight Relaxation of Rank Minimization.
IEEE Trans. Neural Networks Learn. Syst., September, 2023

Elaborate multi-task subspace learning with discrete group constraint.
Pattern Recognit., July, 2023

Learning an Optimal Bipartite Graph for Subspace Clustering via Constrained Laplacian Rank.
IEEE Trans. Cybern., 2023

Calibrated multi-task subspace learning via binary group structure constraint.
Inf. Sci., 2023

2022
Robust Subspace Clustering With Low-Rank Structure Constraint.
IEEE Trans. Knowl. Data Eng., 2022

Adaptive-order proximity learning for graph-based clustering.
Pattern Recognit., 2022

Self-weighted learning framework for adaptive locality discriminant analysis.
Pattern Recognit., 2022

2021
Adaptive Feature Weight Learning For Robust Clustering Problem with Sparse Constraint.
Proceedings of the IEEE International Conference on Acoustics, 2021

New Tight Relaxations of Rank Minimization for Multi-Task Learning.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020
Multi-view spectral clustering via sparse graph learning.
Neurocomputing, 2020

2019
Robust Subspace Clustering by Learning an Optimal Structured Bipartite Graph via Low-rank Representation.
Proceedings of the IEEE International Conference on Acoustics, 2019


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