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143
Exact Indexing of Dynamic Time Warping
, 2002
"... The problem of indexing time series has attracted much research interest in the database community. Most algorithms used to index time series utilize the Euclidean distance or some variation thereof. However is has been forcefully shown that the Euclidean distance is a very brittle distance me ..."
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Cited by 339 (33 self)
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The problem of indexing time series has attracted much research interest in the database community. Most algorithms used to index time series utilize the Euclidean distance or some variation thereof. However is has been forcefully shown that the Euclidean distance is a very brittle distance measure. Dynamic Time Warping (DTW) is a much more robust distance measure for time series, allowing similar shapes to match even if they are out of phase in the time axis.
Analyzing time series gene expression data
 Bioinformatics
, 2004
"... doi:10.1093/bioinformatics/bth283 ..."
I.: Continuous representations of timeseries gene expression data
 J Comput Biol
"... We present algorithms for timeseries gene expression analysis that permit the principled estimation of unobserved time points, clustering, and dataset alignment. Each expression pro � le is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time poin ..."
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Cited by 95 (12 self)
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We present algorithms for timeseries gene expression analysis that permit the principled estimation of unobserved time points, clustering, and dataset alignment. Each expression pro � le is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point in � uences the overall smooth expression curve. We constrain the spline coef � cients of genes in the same class to have similar expression patterns, while also allowing for gene speci � c parameters. We show that unobserved time points can be reconstructed using our method with 10–15 % less error when compared to previous best methods. Our clustering algorithm operates directly on the continuous representations of gene expression pro � les, and we demonstrate that this is particularly effective when applied to nonuniformly sampled data. Our continuous alignment algorithm also avoids dif � culties encountered by discrete approaches. In particular, our method allows for control of the number of degrees of freedom of the warp through the speci � cation of parameterized functions, which helps to avoid over � tting. We demonstrate that our algorithm produces stable lowerror alignments on real expression data and further show a speci � c application to yeast knockout data that produces biologically meaningful results. Key words: time series expression data, missing value estimation, clustering, alignment. 1.
A new approach to analyzing gene expression time series data
, 2002
"... 1 Introduction Principled methods for estimating unobserved timepoints,clustering, and aligning microarray gene expression timeseries are needed to make such data useful for detailed analysis. Datasets measuring temporal behavior of thousands of genes offer rich opportunities for computational bio ..."
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Cited by 90 (5 self)
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1 Introduction Principled methods for estimating unobserved timepoints,clustering, and aligning microarray gene expression timeseries are needed to make such data useful for detailed analysis. Datasets measuring temporal behavior of thousands of genes offer rich opportunities for computational biologists. For example, Dynamic Bayesian Networks may be usedto build models and try to understand how genetic responses unfold. However, such modeling frameworks need a sufficient quantity of data in the appropriate format. Current gene expression timeseries data often do not meet these requirements, since they may be missing data points, sampled nonuniformly, and measure biological processes that exhibittemporal variation.
Making Timeseries Classification More Accurate Using Learned Constraints
, 2004
"... It has long been known that Dynamic Time Warping (DTW) is superior to Euclidean distance for classification and clustering of time series. However, until lately, most research has utilized Euclidean distance because it is more efficiently calculated. A recently introduced technique that greatly miti ..."
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Cited by 79 (18 self)
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It has long been known that Dynamic Time Warping (DTW) is superior to Euclidean distance for classification and clustering of time series. However, until lately, most research has utilized Euclidean distance because it is more efficiently calculated. A recently introduced technique that greatly mitigates DTWs demanding CPU time has sparked a flurry of research activity. However, the technique and its many extensions still only allow DTW to be applied to moderately large datasets. In addition, almost all of the research on DTW has focused exclusively on speeding up its calculation; there has been little work done on improving its accuracy. In this work, we target the accuracy aspect of DTW performance and introduce a new framework that learns arbitrary constraints on the warping path of the DTW calculation. Apart from improving the accuracy of classification, our technique as a side effect speeds up DTW by a wide margin as well. We show the utility of our approach on datasets from diverse domains and demonstrate significant gains in accuracy and efficiency.
Analysis Techniques for Microarray TimeSeries Data (Extended Abstract)
 J. Comput. Biol
, 2000
"... Vladimir Filkov Steven Skiena Jizu Zhi Dept. of Computer Science and Center for Biotechnology State University of New York Stony Brook, NY 117944400 fvl lkovskienazjizug@cs.sunysb.edu September 27, 2000 1 ..."
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Cited by 65 (3 self)
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Vladimir Filkov Steven Skiena Jizu Zhi Dept. of Computer Science and Center for Biotechnology State University of New York Stony Brook, NY 117944400 fvl lkovskienazjizug@cs.sunysb.edu September 27, 2000 1
Indexing large humanmotion databases
 In Proc. 30th VLDB Conf
, 2004
"... Datadriven animation has become the industry standard for computer games and many animated movies and special effects. In particular, motion capture data recorded from live actors, is the most promising approach offered thus far for animating realistic human characters. However, the manipulation of ..."
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Cited by 65 (6 self)
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Datadriven animation has become the industry standard for computer games and many animated movies and special effects. In particular, motion capture data recorded from live actors, is the most promising approach offered thus far for animating realistic human characters. However, the manipulation of such data for general use and reuse is not yet a solved problem. Many of the existing techniques dealing with editing motion rely on indexing for annotation, segmentation, and reordering of the data. Euclidean distance is inappropriate for solving these indexing problems because of the inherent variability found in human motion. The limitations of Euclidean distance stems from the fact that it is very sensitive to distortions in the time axis. A partial solution to this problem, Dynamic Time Warping (DTW), aligns the time axis
Path similarity skeleton graph matching
 IEEE TRANS. PAMI
, 2008
"... This paper proposes a novel graph matching algorithm and applies it to shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we do not consider the to ..."
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Cited by 53 (8 self)
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This paper proposes a novel graph matching algorithm and applies it to shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we do not consider the topological graph structure. Our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures. The proposed comparison of geodesic paths between endpoints of skeleton graphs yields correct matching results in such cases. The skeletons are pruned by contour partitioning with Discrete Curve Evolution, which implies that the endpoints of skeleton branches correspond to visual parts of the objects. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and contour deformations.
Three Myths about Dynamic Time Warping Data
 Mining, in the Proceedings of SIAM International Conference on Data Mining (2005
"... The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a nonlinear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for va ..."
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Cited by 42 (14 self)
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The Dynamic Time Warping (DTW) distance measure is a technique that has long been known in speech recognition community. It allows a nonlinear mapping of one signal to another by minimizing the distance between the two. A decade ago, DTW was introduced into Data Mining community as a utility for various tasks for time series problems including classification, clustering, and anomaly detection. The technique has flourished, particularly in the last three years, and has been applied to a variety of problems in various disciplines. In spite of DTW’s great success, there are still several persistent “myths ” about it. These myths have caused confusion and led to much wasted research effort. In this work, we will dispel these myths with the most comprehensive set of time series experiments ever conducted.
Functional Modeling and Classification of Longitudinal Data
"... We review and extend some statistical tools that have proved useful for analyzing functional data. Functional data analysis primarily is designed for the analysis of random trajectories and infinitedimensional data, and there exists a need for the development of adequate statistical estimation and ..."
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Cited by 40 (11 self)
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We review and extend some statistical tools that have proved useful for analyzing functional data. Functional data analysis primarily is designed for the analysis of random trajectories and infinitedimensional data, and there exists a need for the development of adequate statistical estimation and inference techniques. While this field is in flux, some methods have proven useful. These include warping methods, functional principal component analysis, and conditioning under Gaussian assumptions for the case of sparse data. The latter is a recent development that may provide a bridge between functional and more classical longitudinal data analysis. Besides presenting a brief review of functional principal components and functional regression, we develop some concepts for estimating functional principal component scores in the sparse situation. An extension of the socalled generalized functional linear model to the case of sparse longitudinal predictors is proposed. This extension includes functional binary regression models for longitudinal data and is illustrated with data on primary biliary cirrhosis.