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The problem of finding unusual time series has recently attracted much attention, and several promising methods are now in the literature. However, virtually all proposed methods assume that the data reside in main memory. For many real-world problems this is not be the case. For example, in astronomy, multi-terabyte time series datasets are the norm. Most(More)
Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single(More)
hat do citation screening for evidence-based medicine and generating land-cover maps of the Earth have in common? Both are real-world problems for which we have applied machine-learning techniques to assist human experts, and in each case doing so has motivated the development of novel machine-learning methods. Our research group works closely with domain(More)
—Onboard classification of remote sensing data is of general interest given that it can be used as a trigger to initiate alarms, data download, additional higher-resolution scans, or more frequent scans of an area without ground interaction. In our case, we study the sulfur-rich Borup-Fiord glacial springs in Canada utilizing the Hyperion instrument aboard(More)
The quantity of astronomical observations collected by today's instruments far exceeds the capability of manual inspection by domain experts. Rather than relying on human eyes to examine and analyze all collected data, we employ data triage algorithms shortly after data collection. Automated data triage enables increased science return by prioritizing(More)
—This paper presents PWEM, a technique for detecting class label noise in training data. PWEM detects mislabeled examples by assigning to each training example a probability that its label is correct. PWEM calculates this probability by clustering examples from pairs of classes together and analyzing the distribution of labels within each cluster to derive(More)
Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing(More)