Training linear ranking SVMs in linearithmic time using red-black trees

TL;DR

This paper presents a novel, efficient algorithm for training linear ranking SVMs that combines cutting plane optimization with red-black trees, achieving linearithmic time complexity and superior scalability.

Contribution

It introduces a new method that enables training linear ranking SVMs in linearithmic time using red-black trees, surpassing previous algorithms in efficiency and flexibility.

Findings

Achieves O(m*s + m*log(m)) training time complexity.

Demonstrates superior scalability over existing RankSVM implementations.

Supports real-valued utility scores in training data.

Abstract

We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(m*s+m*log(m)) time complexity, where m is the number of training examples, and s the average number of non-zero features per example. Best previously known training algorithms achieve the same efficiency only for restricted special cases, whereas the proposed approach allows any real valued utility scores in the training data. Experiments demonstrate the superior scalability of the proposed approach, when compared to the fastest existing RankSVM implementations.