Role-behavior analysis from trajectory data by cross-domain learning

Shin Ando, Einoshin Suzuki

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

5 被引用数 (Scopus)

抄録

Behavior analysis using trajectory data presents a practical and interesting challenge for KDD. Conventional analyses address discriminative tasks of behaviors, e.g., classification and clustering typically using the subsequences extracted from the trajectory of an object as a numerical feature representation. In this paper, we explore further to identify the difference in the high-level semantics of behaviors such as roles and address the task in a cross-domain learning approach. The trajectory, from which the features are sampled, is intuitively viewed as a domain, and we assume that its intrinsic structure is characterized by the underlying role associated with the tracked object. We propose a novel hybrid method of spectral clustering and density approximation for comparing clustering structures of two independently sampled trajectory data and identifying patterns of behaviors unique to a role. We present empirical evaluations of the proposed method in two practical settings using real-world robotic trajectories.

本文言語英語
ホスト出版物のタイトルProceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
ページ21-30
ページ数10
DOI
出版ステータス出版済み - 2011
イベント11th IEEE International Conference on Data Mining, ICDM 2011 - Vancouver, BC, カナダ
継続期間: 12月 11 201112月 14 2011

出版物シリーズ

名前Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN(印刷版)1550-4786

その他

その他11th IEEE International Conference on Data Mining, ICDM 2011
国/地域カナダ
CityVancouver, BC
Period12/11/1112/14/11

!!!All Science Journal Classification (ASJC) codes

  • 工学(全般)

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