TY - JOUR
T1 - Implicit Weight Learning for Multi-View Clustering
AU - Nie, Feiping
AU - Shi, Shaojun
AU - Li, Jing
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2023/8/1
Y1 - 2023/8/1
N2 - Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. In general, it is essential to measure the importance of each individual view, due to some noises, or inherent capacities in the description. Many previous works model the view importance as weight, which is simple but effective empirically. In this article, instead of following the traditional thoughts, we propose a new weight learning paradigm in the context of multi-view clustering in virtue of the idea of the reweighted approach, and we theoretically analyze its working mechanism. Meanwhile, as a carefully achieved example, all of the views are connected by exploring a unified Laplacian rank constrained graph, which will be a representative method to compare with other weight learning approaches in experiments. Furthermore, the proposed weight learning strategy is much suitable for multi-view data, and it can be naturally integrated with many existing clustering learners. According to the numerical experiments, the proposed implicit weight learning approach is proven effective and practical to use in multi-view clustering.
AB - Exploiting different representations, or views, of the same object for better clustering has become very popular these days, which is conventionally called multi-view clustering. In general, it is essential to measure the importance of each individual view, due to some noises, or inherent capacities in the description. Many previous works model the view importance as weight, which is simple but effective empirically. In this article, instead of following the traditional thoughts, we propose a new weight learning paradigm in the context of multi-view clustering in virtue of the idea of the reweighted approach, and we theoretically analyze its working mechanism. Meanwhile, as a carefully achieved example, all of the views are connected by exploring a unified Laplacian rank constrained graph, which will be a representative method to compare with other weight learning approaches in experiments. Furthermore, the proposed weight learning strategy is much suitable for multi-view data, and it can be naturally integrated with many existing clustering learners. According to the numerical experiments, the proposed implicit weight learning approach is proven effective and practical to use in multi-view clustering.
KW - Graph-based clustering
KW - multi-view clustering
KW - rank constraint
KW - weight learning
UR - http://www.scopus.com/inward/record.url?scp=85118682470&partnerID=8YFLogxK
U2 - 10.1109/TNNLS.2021.3121246
DO - 10.1109/TNNLS.2021.3121246
M3 - 文章
C2 - 34723810
AN - SCOPUS:85118682470
SN - 2162-237X
VL - 34
SP - 4223
EP - 4236
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 8
ER -