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arXiv · 1610.02588

Iterative proportional scaling revisited: a modern optimization perspective

Yiyuan She·Shao Tang
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Abstract

This paper revisits the classic iterative proportional scaling (IPS) from a modern optimization perspective. In contrast to the criticisms made in the literature, we show that based on a coordinate descent characterization, IPS can be slightly modified to deliver coefficient estimates, and from a majorization-minimization standpoint, IPS can be extended to handle log-affine models with features not necessarily binary-valued or nonnegative. Furthermore, some state-of-the-art optimization techniques such as block-wise computation, randomization and momentum-based acceleration can be employed to provide more scalable IPS algorithms, as well as some regularized variants of IPS for concurrent feature selection.

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Yiyuan She, Shao Tang. 2016-10-08. Iterative proportional scaling revisited: a modern optimization perspective. https://arxiv.org/abs/1610.02588

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