Probabilistic Nuclear-Norm Matrix Regression Regularized by Random Graph Theory-Reference-Cited by-同舟云学术

Probabilistic Nuclear-Norm Matrix Regression Regularized by Random Graph Theory

Published:2024-08 Issue:4 Volume:8 Page:2762-2774
ISSN:2471-285X
Container-title:IEEE Transactions on Emerging Topics in Computational Intelligence
language:
Short-container-title:IEEE Trans. Emerg. Top. Comput. Intell.

Author:

Zhou Jianhang¹^ORCID,Li Shuyi²^ORCID,Zeng Shaoning³^ORCID,Zhang Bob⁴^ORCID

Affiliation:

1. Department of Intelligent Media, Institute of Scientific and Industrial Research (SANKEN), Osaka University, Suita, Japan

2. Faculty of Information Technology, Beijing University of Technology, Beijing, China

3. Yangtze Delta Region Institute (Hu Zhou), University of Electronic Science and Technology of China, Zhejiang, China

4. Pattern Analysis and Machine Intelligence Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau SAR, China

Funder

The Science and Technology Development Fund, Macao S.A.R

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7433297/10607834/10477563.pdf?arnumber=10477563

Reference51 articles.

1. Computational Intelligence in Automatic Face Age Estimation: A Survey

2. Integrating SIFT and CNN Feature Matching for Partial-Duplicate Image Detection

3. Visual to Text: Survey of Image and Video Captioning

4. High-Dimensional Data Analysis with Low-Dimensional Models

5. Structured sparse principal component analysis;Jenatton,2010

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