Subspace Clustering via Adaptive Non-Negative Representation Learning and Its Application to Image Segmentation-Reference-Cited by-同舟云学术

Subspace Clustering via Adaptive Non-Negative Representation Learning and Its Application to Image Segmentation

Published:2023-08 Issue:8 Volume:33 Page:4177-4189
ISSN:1051-8215
Container-title:IEEE Transactions on Circuits and Systems for Video Technology
language:
Short-container-title:IEEE Trans. Circuits Syst. Video Technol.

Author:

Zhao Yin-Ping¹^ORCID,Dai Xiangfeng²^ORCID,Wang Zhen²^ORCID,Li Xuelong³^ORCID

Affiliation:

1. School of Software, Northwestern Polytechnical University, Xi’an, China

2. School of Mechanical Engineering and the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China

3. School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi’an, China

Funder

National Natural Science Foundation for Distinguished Young Scholars

National Natural Science Foundation of China

Fok Ying Tong Education Foundation

Technological Innovation Team of Shaanxi Province

Tencent Foundation and XPLORER PRIZE

Fundamental Research Funds for the Central Universities

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Link

http://xplorestaging.ieee.org/ielx7/76/10207864/10032568.pdf?arnumber=10032568

Reference74 articles.

1. Generalized Latent Multi-View Subspace Clustering

2. A note on the convergence of ADMM for linearly constrained convex optimization problems

3. Learning a Joint Affinity Graph for Multiview Subspace Clustering

4. Robust Graph Regularized Nonnegative Matrix Factorization for Clustering

5. Sparse Subspace Clustering: Algorithm, Theory, and Applications

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