Kernel principal component analysis
Advances in kernel methods
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
EOSDIS Petabyte Archives: Tenth Anniversary
MSST '05 Proceedings of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Moving Objects Databases (The Morgan Kaufmann Series in Data Management Systems) (The Morgan Kaufmann Series in Data Management Systems)
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Proceedings of the VLDB Endowment
Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry-aware metric learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Fast Similarity Search for Learned Metrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-supervised metric learning using pairwise constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A framework for moving sensor data query and retrieval of dynamic atmospheric events
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Neurocomputing
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The Earth Observing System Data and Information System (EOSDIS) is a comprehensive data and information system which archives, manages, and distributes Earth science data from the EOS spacecrafts. One non-existent capability in the EOSDIS is the retrieval of satellite sensor data based on weather events (such as tropical cyclones) similarity query output. In this paper, we propose a framework to solve the similarity search problem given user-defined instance-level constraints for tropical cyclone events, represented by arbitrary length multidimensional spatio-temporal data sequences. A critical component for such a problem is the similarity/metric function to compare the data sequences. We describe a novel Longest Common Subsequence (LCSS) parameter learning approach driven by nonlinear dimensionality reduction and distance metric learning. Intuitively, arbitrary length multidimensional data sequences are projected into a fixed dimensional manifold for LCSS parameter learning. Similarity search is achieved through consensus among the (similar) instance-level constraints based on ranking orders computed using the LCSS-based similarity measure. Experimental results using a combination of synthetic and real tropical cyclone event data sequences are presented to demonstrate the feasibility of our parameter learning approach and its robustness to variability in the instance constraints. We, then, use a similarity query example on real tropical cyclone event data sequences from 2000 to 2008 to discuss (i) a problem of scientific interest, and (ii) challenges and issues related to the weather event similarity search problem.