TY - JOUR
T1 - Two graph-regularized fuzzy subspace clustering methods
AU - Teng, Yueyang
AU - Qi, Shouliang
AU - Han, Fangfang
AU - Xu, Lisheng
AU - Yao, Yudong
AU - Qian, Wei
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2021/3
Y1 - 2021/3
N2 - This paper presents a new fuzzy subspace clustering (FSC) method which finds some subspaces as clusters such that each point belongs to the nearest subspace with a certain weight or probability. Then we propose two graph-regularized versions for it, in which two points are more likely to be assigned to the same cluster if they are close spatially or with the same labels. In the proposed two graph regularizations, one encodes the weight (or probability) of a point assigned to a cluster and the other encodes the projection coefficients of a point on a subspace. We develop iterative solutions for these methods through constructing a surrogate, which monotonically decrease the cost function with a simple structure. The experimental results, using both synthetic and real-world databases, demonstrate the effectiveness and flexibility of the proposed methods.
AB - This paper presents a new fuzzy subspace clustering (FSC) method which finds some subspaces as clusters such that each point belongs to the nearest subspace with a certain weight or probability. Then we propose two graph-regularized versions for it, in which two points are more likely to be assigned to the same cluster if they are close spatially or with the same labels. In the proposed two graph regularizations, one encodes the weight (or probability) of a point assigned to a cluster and the other encodes the projection coefficients of a point on a subspace. We develop iterative solutions for these methods through constructing a surrogate, which monotonically decrease the cost function with a simple structure. The experimental results, using both synthetic and real-world databases, demonstrate the effectiveness and flexibility of the proposed methods.
KW - Fuzzy c-means (FCM)
KW - Fuzzy subspace clustering (FSC)
KW - Gaussian mixture model (GMM)
KW - Graph regularization
KW - Surrogate
UR - http://www.scopus.com/inward/record.url?scp=85097579334&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097579334&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2020.106981
DO - 10.1016/j.asoc.2020.106981
M3 - Article
AN - SCOPUS:85097579334
SN - 1568-4946
VL - 100
JO - Applied Soft Computing
JF - Applied Soft Computing
M1 - 106981
ER -