Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Skeleton-Based Motion Capture for Robust Reconstruction of Human Motion
CA '00 Proceedings of the Computer Animation
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Adaptive stream resource management using Kalman Filters
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Example-based control of human motion
SCA '04 Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation
Performance animation from low-dimensional control signals
ACM SIGGRAPH 2005 Papers
Capturing and animating skin deformation in human motion
ACM SIGGRAPH 2006 Papers
Estimation of missing markers in human motion capture
The Visual Computer: International Journal of Computer Graphics
On Trajectory Representation for Scientific Features
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Adaptive, hands-off stream mining
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
iSAX: indexing and mining terabyte sized time series
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Classifying Data Streams with Skewed Class Distributions and Concept Drifts
IEEE Internet Computing
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Parsimonious linear fingerprinting for time series
Proceedings of the VLDB Endowment
Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
ThermoCast: a cyber-physical forecasting model for datacenters
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Bilinear spatiotemporal basis models
ACM Transactions on Graphics (TOG)
Continuously identifying representatives out of massive streams
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
Modeling multivariate spatio-temporal remote sensing data with large gaps
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Rise and fall patterns of information diffusion: model and implications
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
RainMon: an integrated approach to mining bursty timeseries monitoring data
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern discovery in data streams under the time warping distance
The VLDB Journal — The International Journal on Very Large Data Bases
A temporal pattern mining approach for classifying electronic health record data
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Given multiple time sequences with missing values, we propose DynaMMo which summarizes, compresses, and finds latent variables. The idea is to discover hidden variables and learn their dynamics, making our algorithm able to function even when there are missing values. We performed experiments on both real and synthetic datasets spanning several megabytes, including motion capture sequences and chlorine levels in drinking water. We show that our proposed DynaMMo method (a) can successfully learn the latent variables and their evolution; (b) can provide high compression for little loss of reconstruction accuracy; (c) can extract compact but powerful features for segmentation, interpretation, and forecasting; (d) has complexity linear on the duration of sequences.