Work

An Outlier-Resilient Autoencoder for Representing High-Dimensional and Incomplete Data

Year: 2024

Type: article

Source: IEEE Transactions on Emerging Topics in Computational Intelligence

DOI: 10.1109/tetci.2024.3437370

Authors: Di Wu, Yuanpeng Hu, Kechen Liu, Jing Li, Xianmin Wang, Song Deng, Nenggan Zheng, Xin Luo

Institutions: Southwest University, Guangzhou University, Columbia University, Nanjing University of Posts and Telecommunications, Nanjing University, Zhejiang University

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

Funders: National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province, Natural Science Foundation of Chongqing

Awards: G2333429615, G309624659, G3545694276, G3603755728, 对抗环境下机器学习安全与隐私保护关键技术研究, G6232437620, G7355380096, G7576591565, G8515641209

OpenAlex ID: W4401507368

FWCI: 3.192

Cites: 53

Cited by: 21

Related to: 10