TY - JOUR
T1 - Parameter-Free Weighted Multi-View Projected Clustering with Structured Graph Learning
AU - Wang, Rong
AU - Nie, Feiping
AU - Wang, Zhen
AU - Hu, Haojie
AU - Li, Xuelong
N1 - Publisher Copyright:
© 1989-2012 IEEE.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - In many real-world applications, we are often confronted with high dimensional data which are represented by various heterogeneous views. How to cluster this kind of data is still a challenging problem due to the curse of dimensionality and effectively integration of different views. To address this problem, we propose two parameter-free weighted multi-view projected clustering methods which perform structured graph learning and dimensionality reduction simultaneously. We can use the obtained structured graph directly to extract the clustering indicators, without performing other discretization procedures as previous graph-based clustering methods have to do. Moreover, two parameter-free strategies are adopted to learn an optimal weight for each view automatically, without introducing a regularization parameter as previous methods do. Extensive experiments on several public datasets demonstrate that the proposed methods outperform other state-of-the-art approaches and can be used more practically.
AB - In many real-world applications, we are often confronted with high dimensional data which are represented by various heterogeneous views. How to cluster this kind of data is still a challenging problem due to the curse of dimensionality and effectively integration of different views. To address this problem, we propose two parameter-free weighted multi-view projected clustering methods which perform structured graph learning and dimensionality reduction simultaneously. We can use the obtained structured graph directly to extract the clustering indicators, without performing other discretization procedures as previous graph-based clustering methods have to do. Moreover, two parameter-free strategies are adopted to learn an optimal weight for each view automatically, without introducing a regularization parameter as previous methods do. Extensive experiments on several public datasets demonstrate that the proposed methods outperform other state-of-the-art approaches and can be used more practically.
KW - Multi-view clustering
KW - dimensionality reduction
KW - structured graph learning
UR - http://www.scopus.com/inward/record.url?scp=85091246308&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2019.2913377
DO - 10.1109/TKDE.2019.2913377
M3 - 文章
AN - SCOPUS:85091246308
SN - 1041-4347
VL - 32
SP - 2014
EP - 2025
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 10
M1 - 8700233
ER -