Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization

Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization

Multi-source heterogeneous data has been widely adopted in developing artificial intelligence systems in recent years. In real-world scenarios, raw multi-source data are generally unlabeled and inherently contain multi-view noise and feature redundancy, leading to extensive research on unsupervised...

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Bibliographic Details
Main Authors: Yifan Shi, Haixin Zeng, Xinrong Gong, Lei Cai, Wenjie Xiang, Qi Lin, Huijie Zheng, Jianqing Zhu
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Applied Sciences
Subjects:
unsupervised feature selection
consensus clustering
heterogeneous data
Online Access:https://www.mdpi.com/2076-3417/15/12/6884
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