---
abstract: 'We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available. We propose a new hybrid optimization algorithm that solves the elastic-net support vector machine (SVM) through an alternating direction method of multipliers in the first phase, followed by an interior-point method for the classical SVM in the second phase. Both SVM formulations are adapted to knowledge incorporation. Our proposed algorithm addresses the challenges of automatic feature selection, high optimization accuracy, and algorithmic flexibility for taking advantage of prior knowledge. We demonstrate the effectiveness and efficiency of our algorithm and compare it with existing methods on a collection of synthetic and real-world data.'
author:
- 'Z. Qin'
- 'X. Tang'
- 'I. Akrotirianakis'
- 'A. Chakraborty'
bibliography:
- 'scr\_bib.bib'
title: 'HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection'
---

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Introduction {#sec:motivation}
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Classification tasks on data sets with large feature dimensions are very common in real-world machine learning applications. Typical examples include microarray data for gene selection and text documents for natural language processing. Despite the large number of features present in the data 