Anytime query-tuned kernel machines via Cholesky factorization. Proceedings of SIAM International Conference on Data Mining

SIAMDM03, May 2003
Anytime query-tuned kernel machines via Cholesky factorization. Proceedings of SIAM International Conference on Data Mining
Dennis DeCoste, Dennis DeCoste

Kernel machines (including support vector machines) offer powerful new methods for improving the accuracy and robustness of fundamental data mining operations on challenging (e.g. high-dimensional) data, including classification, regression, dimensionality reduction, and outlier detection.

However, a key tradeoff to this power is that kernel machines typically compute their outputs in terms of a large fraction of the training data, making it difficult to scale them up to train and run over massive data sets typically tackled in data mining contexts.

We recently demonstrated 2 to 64-fold querytime speedups of SVM and Kernel Fisher classifiers via a new computational geometry method for anytime output bounds [4]. This new paper refines our approach in two key ways.

First, we introduce a simple linear algebra formulation based on standard Cholesky factorization, yielding simpler equations and lower computational overhead. Second, this new formulation suggests new methods for achieving additional speedups, including tuning on query samples. We demonstrate effectiveness on three benchmark datasets.

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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.