Data-Pattern Discovery Methods for Detection in Nongaussian High-dimensional Data Sets

Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers. pp. 545-549. 2005
Data-Pattern Discovery Methods for Detection in Nongaussian High-dimensional Data Sets
Cécile Levasseur, Ken Kreutz-Delgado, Uwe Mayer, Gregory Gancarz
eBay Authors
Abstract

Many important expert system applications depend on the ability to accurately detect or predict the occurrence of key events given a data set of observations. We concentrate on multidimensional data that are highly nongaussian (continuous and/or discrete), noisy and nonlinearly related.

We investigate the feasibility of data-pattern discovery and event detection by applying generalized principal component analysis (GPCA) techniques for pattern extraction based on an exponential family probability distribution assumption.

We develop theoretical extensions of the GPCA model by exploiting results from the theory of generalized linear models and nonparametric mixture density estimation.

Another publication from the same author: Uwe Mayer

Proceedings of the Sixteenth ACM Conference on Economics and Computation (EC '15). ACM, New York, NY, USA (2015)

Canary in the e-Commerce Coal Mine: Detecting and Predicting Poor Experiences Using Buyer-to-Seller Messages

Dimitriy Masterov, Uwe Mayer, Steve Tadelis

Reputation and feedback systems in online marketplaces are often biased, making it difficult to ascertain the quality of sellers. We use post-transaction, buyer-to-seller message traffic to detect signals of unsatisfactory transactions on eBay. We posit that a message sent after the item was paid for serves as a reliable indicator that the buyer may be unhappy with that purchase, particularly when the message included words associated with a negative experience. The fraction of a seller's message traffic that was negative predicts whether a buyer who transacts with this seller will stop purchasing on eBay, implying that platforms can use these messages as an additional signal of seller quality.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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