@article{oai:tsukuba.repo.nii.ac.jp:00035792,
 author = {日野, 英逸 and Koshijima, K. and Hino, H and Murata, N.},
 issue = {10},
 journal = {IEEE transactions on knowledge and data engineering},
 month = {Oct},
 note = {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. After estimating the underlying distribution behind each bag of data and embedding those distributions in a metric space, the change-point score is derived by evaluating how the sequence of distributions is fluctuating in the metric space using a distance-based information estimator. Also, a procedure that adaptively determines when to raise alerts is incorporated by calculating the confidence interval of the change-point score at each time step. This avoids raising false alarms in highly noisy situations and enables detecting changes of various magnitudes. A number of experimental studies and numerical examples are provided to demonstrate the generality and the effectiveness of our approach with both synthetic and real datasets.},
 pages = {2632--2644},
 title = {Change-Point Detection in a Sequence of Bags-of-Data},
 volume = {27},
 year = {2015}
}