TY - GEN

T1 - Semi-supervised classification using multiple clustering and low-rank matrix operations

AU - Berikov, Vladimir

PY - 2019/1/1

Y1 - 2019/1/1

N2 - This paper proposes a semi-supervised classification method which combines machine learning regularization framework and cluster ensemble approach. We use the low-rank decomposition of the co-association matrix of the ensemble to significantly speed up calculations and save memory. Numerical experiments using Monte Carlo approach demonstrate the efficiency of the proposed method.

AB - This paper proposes a semi-supervised classification method which combines machine learning regularization framework and cluster ensemble approach. We use the low-rank decomposition of the co-association matrix of the ensemble to significantly speed up calculations and save memory. Numerical experiments using Monte Carlo approach demonstrate the efficiency of the proposed method.

KW - Cluster ensemble

KW - Co-association matrix

KW - Low-rank matrix decomposition

KW - Regularization

KW - Semi-supervised classification

UR - http://www.scopus.com/inward/record.url?scp=85067677189&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-22629-9_37

DO - 10.1007/978-3-030-22629-9_37

M3 - Conference contribution

AN - SCOPUS:85067677189

SN - 9783030226282

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 529

EP - 540

BT - Mathematical Optimization Theory and Operations Research - 18th International Conference, MOTOR 2019, Proceedings

A2 - Khachay, Michael

A2 - Pardalos, Panos

A2 - Kochetov, Yury

PB - Springer-Verlag GmbH and Co. KG

T2 - 18th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2019

Y2 - 8 July 2019 through 12 July 2019

ER -