---
title: "Soft Margin Consistency Based Scalable Multi-View Maximum Entropy Discrimination"
authors:
  - "Liang Mao"
  - "Shiliang Sun"
doi: ""
url: "https://ijcai.org/proceedings/2016/263"
pdf: "https://ijcai.org/proceedings/2016/0263.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "1839-1844"
track: ""
keywords:
---

# Soft Margin Consistency Based Scalable Multi-View Maximum Entropy Discrimination

Liang Mao, Shiliang Sun

*Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence*, pages 1839-1844.

## Abstract

Multi-view learning receives increasing interest in recent years to analyze complex data. Lately, multi-view maximum entropy discrimination (MVMED) and alternative MVMED (AMVMED) were proposed as extensions of maximum entropy discrimination (MED) to the multi-view learning setting, which use the hard margin consistency principle that enforces two view margins to be the same. In this paper, we propose soft margin consistency based multi-view MED (SMVMED) achieving margin consistency in a less strict way, which minimizes the relative entropy between the posteriors of two view margins. With a trade-off parameter balancing large margin and margin consistency, SMVMED is more flexible. We also propose a sequential minimal optimization (SMO) algorithm to efficiently train SMVMED and make it scalable to large datasets. We evaluate the performance of SMVMED on multiple real-world datasets and get encouraging results.