# Linear Learning with Sparse Data

arXiv: https://arxiv.org/abs/1612.09147 · HTML: https://mlsanity.com/paper/1612.09147/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2016.

## Built on by (most cited first, 1 of 1)

- [Stochastic gradient descent with gradient estimator for categorical features](https://mlsanity.com/paper/2209.03771/) (2022)

## Abstract (arXiv)

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).