TY - GEN
T1 - Principal points estimation using mixture distributions
AU - Ueki, Daichi
AU - Matsuura, Shun
AU - Suzuki, Hideo
PY - 2012/12/1
Y1 - 2012/12/1
N2 - The k-principal points of a distribution are the k points that optimally partition the distribution. In this paper, we propose a method to estimate principal points from data by using mixture distributions when we have no prior knowledge of the distribution of data. Several simulation results are presented to compare the proposed method with the nonparametric k-means.
AB - The k-principal points of a distribution are the k points that optimally partition the distribution. In this paper, we propose a method to estimate principal points from data by using mixture distributions when we have no prior knowledge of the distribution of data. Several simulation results are presented to compare the proposed method with the nonparametric k-means.
UR - http://www.scopus.com/inward/record.url?scp=84875120335&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875120335&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84875120335
SN - 9789791421157
T3 - 2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
SP - 219
EP - 222
BT - 2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings
T2 - 2012 4th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012
Y2 - 1 December 2012 through 2 December 2012
ER -