TY - GEN
T1 - Change-point detection in a sequence of bags-of-data
AU - Koshijima, Kensuke
AU - Hino, Hideitsu
AU - Murata, Noboru
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/6/22
Y1 - 2016/6/22
N2 - In this paper, the limitation that is prominent in most existing works of change-point detection methods is addressed by proposing a nonparametric, computationally efficient method. The limitation is that most works assume that each data point observed at each time step is a single multi-dimensional vector. However, there are many situations where this does not hold. Therefore, a setting where each observation is a collection of random variables, which we call a bag of data, is considered.
AB - In this paper, the limitation that is prominent in most existing works of change-point detection methods is addressed by proposing a nonparametric, computationally efficient method. The limitation is that most works assume that each data point observed at each time step is a single multi-dimensional vector. However, there are many situations where this does not hold. Therefore, a setting where each observation is a collection of random variables, which we call a bag of data, is considered.
UR - http://www.scopus.com/inward/record.url?scp=84980351198&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84980351198&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2016.7498425
DO - 10.1109/ICDE.2016.7498425
M3 - Conference contribution
AN - SCOPUS:84980351198
T3 - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
SP - 1560
EP - 1561
BT - 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE International Conference on Data Engineering, ICDE 2016
Y2 - 16 May 2016 through 20 May 2016
ER -