Causal patterns: Extraction of multiple causal relationships by mixture of probabilistic partial canonical correlation analysis

Hiroki Mori, Keisuke Kawano, Hiroki Yokoyama

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, we propose a mixture of probabilistic partial canonical correlation analysis (MPPCCA) that extracts the Causal Patterns from two multivariate time series. Causal patterns refer to the signal patterns within interactions of two elements having multiple types of mutually causal relationships, rather than a mixture of simultaneous correlations or the absence of presence of a causal relationship between the elements. In multivariate statistics, partial canonical correlation analysis (PCCA) evaluates the correlation between two multivariates after subtracting the effect of the third multivariate. PCCA can calculate the Granger Causality Index (which tests whether a time-series can be predicted from another time-series), but is not applicable to data containing multiple partial canonical correlations. After introducing the MPPCCA, we propose an expectation-maxmization (EM) algorithm that estimates the parameters and latent variables of the MPPCCA. The MPPCCA is expected to extract multiple partial canonical correlations from data series without any supervised signals to split the data as clusters. The method was then evaluated in synthetic data experiments. In the synthetic dataset, our method estimated the multiple partial canonical correlations more accurately than the existing method. To determine the types of patterns detectable by the method, experiments were also conducted on real datasets. The method estimated the communication patterns In motion-capture data. The MPPCCA is applicable to various type of signals such as brain signals, human communication and nonlinear complex multibody systems.

本文言語English
ホスト出版物のタイトルProceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
出版社Institute of Electrical and Electronics Engineers Inc.
ページ744-754
ページ数11
ISBN(電子版)9781509050048
DOI
出版ステータスPublished - 2017 7 2
イベント4th International Conference on Data Science and Advanced Analytics, DSAA 2017 - Tokyo, Japan
継続期間: 2017 10 192017 10 21

出版物シリーズ

名前Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
2018-January

Conference

Conference4th International Conference on Data Science and Advanced Analytics, DSAA 2017
国/地域Japan
CityTokyo
Period17/10/1917/10/21

ASJC Scopus subject areas

  • 信号処理
  • 情報システムおよび情報管理
  • 統計学、確率および不確実性
  • コンピュータ ネットワークおよび通信

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