Discovering Multivariate Motifs using Subsequence Density Estimation and Greedy Mixture Lea.pdf
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Discovering Multivariate Motifs using Subsequence Density Estimation and
Greedy Mixture Learning
David Minnen and Charles L. Isbell and Irfan Essa and Thad Starner
Georgia Institute of Technology
College of Computing / School of Interactive Computing
Atlanta, GA 30332-0760 USA
{dminn,isbell,irfan,thad}@
Abstract
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 mo-
tif because all of the included subsequences are similar. The
ability to automatically discover such motifs allows intelli-
gent systems to form endogenously meaningful representa-
tions of their environment through unsupervised sensor anal-
ysis. 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 non-
linear temporal warping. We evaluate the performance of our
approach using four data sets from different domains includ-
ing on-body inertial sensors and speech.
Introduction
Our goal is to develop intelligent systems that understand
their environment by autonomously learning new concepts
from their perceptions. In this paper, we address one form
of this problem where the concepts correspond to recur-
ring patterns in the sensory data captured by the intelligent
agent. Such recurring patterns are often referred to as per-
ceptual primitives ormotifs and correspond to sets of similar
subsequences in the time-varying sensor data. For exam-
ple, a motif discovery system could find unknown words or
phonemes in speech data, learn common gestures in video
of sign language, or allow a mobile robot to learn endonge-
nously meaningful rep
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