DTW-Distance Based Kernel for Time Series Data

• NARITA Hiroyuki

  Graduate School of Information Sciences, Hiroshima City University

• SAWAMURA Yasumasa

  Graduate School of Information Sciences, Hiroshima City University

• HAYASHI Akira

  Graduate School of Information Sciences, Hiroshima City University

抄録

One of the advantages of the kernel methods is that they can deal with various kinds of objects, not necessarily vectorial data with a fixed number of attributes. In this paper, we develop kernels for time series data using dynamic time warping (DTW) distances. Since DTW distances are pseudo distances that do not satisfy the triangle inequality, a kernel matrix based on them is not positive semidefinite, in general. We use semidefinite programming (SDP) to guarantee the positive definiteness of a kernel matrix. We present neighborhood preserving embedding (NPE), an SDP formulation to obtain a kernel matrix that best preserves the local geometry of time series data. We also present an out-of-sample extension (OSE) for NPE. We use two applications, time series classification and time series embedding for similarity search, to validate our approach.

収録刊行物

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E92-D (1), 51-58, 2009

  一般社団法人 電子情報通信学会

キーワード

• DTW
• SDP
• SVM

詳細情報

• CRID: 1390282679354455040
• NII論文ID: 10026807096
• NII書誌ID: AA10826272
• DOI: 10.1587/transinf.e92.d.51
• ISSN: 17451361
• ISSN: 09168532
• Web Site: http://www.jstage.jst.go.jp/article/transinf/E92.D/1/E92.D_1_51/_pdf
• 本文言語コード: en
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• 抄録ライセンスフラグ: 使用不可