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Efficient and Accurate Methods for Updating Generalized Linear Models with Multiple Feature Additions

Amit Dhur, har, Marek Petrik; 15(75):2607−2627, 2014.


In this paper, we propose an approach for learning regression models efficiently in an environment where multiple features and data-points are added incrementally in a multi-step process. At each step, any finite number of features maybe added and hence, the setting is not amenable to low rank updates. We show that our approach is not only efficient and optimal for ordinary least squares, weighted least squares, generalized least squares and ridge regression, but also more generally for generalized linear models and lasso regression that use iterated re-weighted least squares for maximum likelihood estimation. Our approach instantiated to linear settings has close relations to the partitioned matrix inversion mechanism based on Schur's complement. For arbitrary regression methods, even a relaxation of the approach is no worse than using the model from the previous step or using a model that learns on the additional features and optimizes the residual of the model at the previous step. Such problems are commonplace in complex manufacturing operations consisting of hundreds of steps, where multiple measurements are taken at each step to monitor the quality of the final product. Accurately predicting if the finished product will meet specifications at each or, at least, important intermediate steps can be extremely useful in enhancing productivity. We further validate our claims through experiments on synthetic and real industrial data sets.

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