Change-Point Detection in a Sequence of Bags-of-Data

Kensuke Koshijima, Hideitsu Hino, Noboru Murata

研究成果: Article査読

2 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号7095580
ページ(範囲)2632-2644
ページ数13
ジャーナルIEEE Transactions on Knowledge and Data Engineering
27
10
DOI
出版ステータスPublished - 2015 10 1

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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