TY - GEN
T1 - Spectral clustering with automatic cluster-number identification via finding sparse eigenvectors
AU - Ogino, Yuto
AU - Yukawa, Masahiro
N1 - Funding Information:
This work was supported in part by JSPS Grants-in-Aids (15K06081, 15K13986, 15H02757).
Publisher Copyright:
© EURASIP 2018.
PY - 2018/11/29
Y1 - 2018/11/29
N2 - Spectral clustering is an empirically successful approach to separating a dataset into some groups with possibly complex shapes based on pairwise affinity. Identifying the number of clusters automatically is still an open issue, although many heuristics have been proposed. In this paper, imposing sparsity on the eigenvectors of graph Laplacian is proposed to attain reasonable approximations of the so-called cluster-indicator-vectors, from which the clusters as well as the cluster number are identified. The proposed algorithm enjoys low computational complexity as it only computes a relevant subset of eigenvectors. It also enjoys better clustering quality than the existing methods, as shown by simulations using nine real datasets.
AB - Spectral clustering is an empirically successful approach to separating a dataset into some groups with possibly complex shapes based on pairwise affinity. Identifying the number of clusters automatically is still an open issue, although many heuristics have been proposed. In this paper, imposing sparsity on the eigenvectors of graph Laplacian is proposed to attain reasonable approximations of the so-called cluster-indicator-vectors, from which the clusters as well as the cluster number are identified. The proposed algorithm enjoys low computational complexity as it only computes a relevant subset of eigenvectors. It also enjoys better clustering quality than the existing methods, as shown by simulations using nine real datasets.
UR - http://www.scopus.com/inward/record.url?scp=85059806403&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85059806403&partnerID=8YFLogxK
U2 - 10.23919/EUSIPCO.2018.8553168
DO - 10.23919/EUSIPCO.2018.8553168
M3 - Conference contribution
AN - SCOPUS:85059806403
T3 - European Signal Processing Conference
SP - 1187
EP - 1191
BT - 2018 26th European Signal Processing Conference, EUSIPCO 2018
PB - European Signal Processing Conference, EUSIPCO
T2 - 26th European Signal Processing Conference, EUSIPCO 2018
Y2 - 3 September 2018 through 7 September 2018
ER -