Linear Learning with Sparse Data

Ofer Dekel

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arXiv · 1612.09147

Linear Learning with Sparse Data

Ofer Dekel

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Abstract

Linear predictors are especially useful when the data is high-dimensional and sparse. One of the standard techniques used to train a linear predictor is the Averaged Stochastic Gradient Descent (ASGD) algorithm. We present an efficient implementation of ASGD that avoids dense vector operations. We also describe a translation invariant extension called Centered Averaged Stochastic Gradient Descent (CASGD).

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Ofer Dekel. 2016-12-29. Linear Learning with Sparse Data. https://arxiv.org/abs/1612.09147

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