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2007

Discovering Multivariate Motifs using Subsequence Density Estimation and Greedy Mixture Learning

12 years 1 months ago
Discovering Multivariate Motifs using Subsequence Density Estimation and Greedy Mixture Learning
The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed of several non-overlapping subsequences and constitutes a motif because all of the included subsequences are similar. The ability to automatically discover such motifs allows intelligent systems to form endogenously meaningful representations of their environment through unsupervised sensor analysis. In this paper, we formulate a unifying view of motif discovery as a problem of locating regions of high density in the space of all time series subsequences. Our approach is efficient (sub-quadratic in the length of the data), requires fewer user-specified parameters than previous methods, and naturally allows variable length motif occurrences and nonlinear temporal warping. We evaluate the performance of our approach using four data sets from different domains including on-body inertial sensors and speech.
David Minnen, Charles Lee Isbell Jr., Irfan A. Ess
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2007
Where AAAI
Authors David Minnen, Charles Lee Isbell Jr., Irfan A. Essa, Thad Starner
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