TY - GEN
T1 - Embedded clustering via robust orthogonal least square discriminant analysis
AU - Zhang, Rui
AU - Nie, Feiping
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/16
Y1 - 2017/6/16
N2 - In this paper, a novel embedded clustering (EC) method is derived from the perspective of extending the supervised orthogonal least square discriminant analysis (OLSDA) method to the unsupervised case, which proves to be closely related to k-means. To achieve more statistical and structural properties, the robust learning of unsupervised OLSDA is investigated to further derive the unsupervised robust OLSDA (ROLSDA) problem. For the convenience of solving the proposed ROLSDA problem, re-weighted counterpart of ROLSDA is utilized with self-adaptive weight, such that the smaller weight would be assigned to the term with larger outliers automatically. Consequently, aforementioned EC method is proposed with not only the robust outliers but also the optimal weighted cluster centroids. Comparative experiments are presented to show the effectiveness of the EC method under the proposed ROLSDA problem.
AB - In this paper, a novel embedded clustering (EC) method is derived from the perspective of extending the supervised orthogonal least square discriminant analysis (OLSDA) method to the unsupervised case, which proves to be closely related to k-means. To achieve more statistical and structural properties, the robust learning of unsupervised OLSDA is investigated to further derive the unsupervised robust OLSDA (ROLSDA) problem. For the convenience of solving the proposed ROLSDA problem, re-weighted counterpart of ROLSDA is utilized with self-adaptive weight, such that the smaller weight would be assigned to the term with larger outliers automatically. Consequently, aforementioned EC method is proposed with not only the robust outliers but also the optimal weighted cluster centroids. Comparative experiments are presented to show the effectiveness of the EC method under the proposed ROLSDA problem.
KW - Embedded clustering
KW - least square discriminant analysis
KW - re-weighted problem
KW - robust learning
UR - http://www.scopus.com/inward/record.url?scp=85023739862&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2017.7952573
DO - 10.1109/ICASSP.2017.7952573
M3 - 会议稿件
AN - SCOPUS:85023739862
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2332
EP - 2336
BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Y2 - 5 March 2017 through 9 March 2017
ER -