Partly Locality Sensitive Hashing を用いた時系列データからの高頻度パターン抽出

小川原 光一, 田邉 康史, 倉爪 亮, 長谷川 勉

研究成果: ジャーナルへの寄稿記事

抜粋

Frequent patterns in time series data are useful clues to learn previously unknown events in an unsupervised way. In this paper, we propose a method for detecting frequent patterns in long time series data efficiently. The major contribution of the paper is two-fold: (1) Partly Locality Sensitive Hashing (PLSH) is proposed to find frequent patterns efficiently and (2) the problem of finding consecutive time frames that have a large number of frequent patterns is formulated as a combinatorial optimization problem which is solved via Dynamic Programming (DP) in polynomial time <i>O</i> (<i>N</i> <sup>1+1/α</sup>) thanks to PLSH where <i>N</i> is the total amount of data. The proposed method was evaluated by detecting frequent whole body motions in a video sequence as well as by detecting frequent everyday manipulation tasks in motion capture data.
元の言語Japanese
ページ(範囲)67-76
ページ数10
ジャーナル日本ロボット学会誌
29
発行部数1
DOI
出版物ステータス出版済み - 1 15 2011

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