TY - GEN
T1 - Clustering high-dimensional data via random sampling and consensus
AU - Traganitis, Panagiotis A.
AU - Slavakis, Konstantinos
AU - Giannakis, Georgios B
PY - 2014/2/5
Y1 - 2014/2/5
N2 - In response to the urgent need for learning tools tuned to big data analytics, the present paper introduces a feature selection approach to efficient clustering of high-dimensional vectors. The resultant method leverages random sampling and consensus (RANSAC) arguments, originally developed for robust regression tasks in computer vision, to yield novel dimensionality reduction schemes. The advocated random sampling and consensus K-means (RSC-Kmeans) algorithm can operate in either batch or sequential modes, with the latter being able to afford lower computational footprint than the former. Extensive numerical tests on synthetic and real datasets highlight the potential of the proposed algorithms, and demonstrate their competitive performance relative to state-of-the-art random projection alternatives.
AB - In response to the urgent need for learning tools tuned to big data analytics, the present paper introduces a feature selection approach to efficient clustering of high-dimensional vectors. The resultant method leverages random sampling and consensus (RANSAC) arguments, originally developed for robust regression tasks in computer vision, to yield novel dimensionality reduction schemes. The advocated random sampling and consensus K-means (RSC-Kmeans) algorithm can operate in either batch or sequential modes, with the latter being able to afford lower computational footprint than the former. Extensive numerical tests on synthetic and real datasets highlight the potential of the proposed algorithms, and demonstrate their competitive performance relative to state-of-the-art random projection alternatives.
KW - Clustering
KW - Feature selection
KW - High-dimensional data
KW - K-means
KW - Random sampling and consensus
UR - http://www.scopus.com/inward/record.url?scp=84949929328&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84949929328&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP.2014.7032128
DO - 10.1109/GlobalSIP.2014.7032128
M3 - Conference contribution
AN - SCOPUS:84949929328
T3 - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
SP - 307
EP - 311
BT - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014
Y2 - 3 December 2014 through 5 December 2014
ER -