@inproceedings{
zhang2024scalable,
title={Scalable Multi-view Unsupervised Feature Selection with Structure Learning and Fusion},
author={Chenglong Zhang and Xinyan Liang and Peng Zhou and Zhaolong Ling and Yingwei Zhang and Xingyu Wu and Weiguo Sheng and Bingbing Jiang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=N7AdbHQFwA}
}
To tackle the high-dimensional data with multiple representations, multi-view unsupervised feature selection has emerged as a significant learning paradigm.
		However, previous methods suffer from the following dilemmas:
		(i) The emphasis is on selecting features to preserve the similarity structure of data, while neglecting the discriminative information in the cluster structure;
		(ii) They often impose the orthogonal constraint on the pseudo cluster labels, disrupting the locality in the cluster label space;
		(iii) Learning the similarity or cluster structure from all samples is likewise time-consuming.
		To this end, a Scalable Multi-view Unsupervised Feature Selection with structure learning and fusion (SMUFS) is proposed to jointly exploit the cluster structure and the similarity relations of data.
		Specifically, SMUFS introduces the sample-view weights to adaptively fuse the membership matrices that indicate cluster structures and serve as the pseudo cluster labels, such that a unified membership matrix across views can be effectively obtained to guide feature selection. Meanwhile, SMUFS performs graph learning from the membership matrix, preserving the locality of cluster labels and improving their discriminative capability.
		Further, an acceleration strategy has been developed to make SMUFS scalable for relatively large-scale data.
		A convergent solution is devised to optimize the formulated problem, and extensive experiments demonstrate the effectiveness and superiority of SMUFS.
Scalable Multi-view Unsupervised Feature Selection with Structure Learning and Fusion
Chenglong Zhang Xinyan Liang Peng Zhou Zhaolong Ling Yingwei Zhang Xingyu Wu Weiguo Sheng Bingbing Jiang