AB  - As data with diverse representations become high-dimensional, multi-view unsupervised feature selection has been an important learning paradigm. Generally, existing methods encounter the following challenges (i) traditional solutions either concatenate different views or introduce extra parameters to weight them, affecting the performance and applicability (ii) emphasis is typically placed on graph construction, yet disregarding the clustering information of data (iii) exploring the similarity structure of all samples from the original features is suboptimal and extremely time-consuming. To solve this dilemma, we propose an efficient multi-view unsupervised feature selection (EMUFS) to construct bipartite graphs between samples and anchors. Specifically, a parameter-free manner is devised to collaboratively fuse the membership matrices and graphs to learn the compatible structure information across all views, naturally balancing different views. Moreover, EMUFS leverages the similarity relations of data in the feature subspace induced by l
AU  - Zhang, C
AU  - Fang, Y
AU  - Liang, X
AU  - Zhang, H
AU  - Zhou, P
AU  - Wu, X
AU  - Yang, J
AU  - Jiang, B
AU  - Sheng, W
DA  - 2024/01/01
DO  - 10.24963/ijcai.2024/602
EP  - 5452
JO  - Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2024/01/01
SP  - 5443
TI  - Efficient Multi view Unsupervised Feature Selection with Adaptive Structure Learning and Inference
Y1  - 2024/01/01
Y2  - 2026/05/10
ER  -