TY - GEN
T1 - Discovering deformable motifs in continuous time series data
AU - Saria, Suchi
AU - Duchi, Andrew
AU - Koller, Daphne
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Continuous time series data often comprise or contain repeated motifs - patterns that have similar shape, and yet exhibit nontrivial variability. Identifying these motifs, even in the presence of variation, is an important subtask in both unsupervised knowledge discovery and constructing useful features for discriminative tasks. This paper addresses this task using a probabilistic framework that models generation of data as switching between a random walk state and states that generate motifs. A motif is generated from a continuous shape template that can undergo non-linear transformations such as temporal warping and additive noise. We propose an unsupervised algorithm that simultaneously discovers both the set of canonical shape templates and a template-specific model of variability manifested in the data. Experimental results on three real-world data sets demonstrate that our model is able to recover templates in data where repeated instances show large variability. The recovered templates provide higher classification accuracy and coverage when compared to those from alternatives such as random projection based methods and simpler generative models that do not model variability. Moreover, in analyzing physiological signals from infants in the ICU, we discover both known signatures as well as novel physiomarkers.
AB - Continuous time series data often comprise or contain repeated motifs - patterns that have similar shape, and yet exhibit nontrivial variability. Identifying these motifs, even in the presence of variation, is an important subtask in both unsupervised knowledge discovery and constructing useful features for discriminative tasks. This paper addresses this task using a probabilistic framework that models generation of data as switching between a random walk state and states that generate motifs. A motif is generated from a continuous shape template that can undergo non-linear transformations such as temporal warping and additive noise. We propose an unsupervised algorithm that simultaneously discovers both the set of canonical shape templates and a template-specific model of variability manifested in the data. Experimental results on three real-world data sets demonstrate that our model is able to recover templates in data where repeated instances show large variability. The recovered templates provide higher classification accuracy and coverage when compared to those from alternatives such as random projection based methods and simpler generative models that do not model variability. Moreover, in analyzing physiological signals from infants in the ICU, we discover both known signatures as well as novel physiomarkers.
UR - http://www.scopus.com/inward/record.url?scp=84881083497&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881083497&partnerID=8YFLogxK
U2 - 10.5591/978-1-57735-516-8/IJCAI11-247
DO - 10.5591/978-1-57735-516-8/IJCAI11-247
M3 - Conference contribution
AN - SCOPUS:84881083497
SN - 9781577355120
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 1465
EP - 1471
BT - IJCAI 2011 - 22nd International Joint Conference on Artificial Intelligence
T2 - 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
Y2 - 16 July 2011 through 22 July 2011
ER -