基于间隔迁移的多视角支持向量机

针对同一对象从不同途径或不同层面获得的特征数据被称为多视角数据. 多视角学习是利用事物的多视角数据进行建模求解的一种新的机器学习方法. 大量研究表明, 多视角数据共同学习可以显著提高模型的学习效果, 因此许多相关模型及算法被提出. 多视角学习一般需遵循一 致性原则和互补性原则. 基于一致性原则,Farquhar 等人成功地将支持向量机(Support Vector Machine, SVM)和核典型相关分析(Kernel Canonical Correlation Analysis, KCCA)整合成一个单独的优化问题, 提出SVM-2K模型. 但是, SVM-2K模型并未充分利用多视角数据间的互补信息. 因此, 在SVM-2K模型的基础之上, 提出了基于间隔迁移的多视角支持向量机模型(Margin transfer-based multi-view support vector machine, M^2SVM), 该模型同时满足多视角学习的一致性和互补 性两原则. 进一步地, 从一致性的角度对其进行理论分析, 并 与SVM-2K比较, 揭示了 M^2SVM 比SVM-2K 更为灵活. 最后, 在大量的多视角数据集上验证了M^2SVM模型的有效性.

Margin transfer-based multi-view support vector machine

The data obtained from multiple sources or different feature subsets are called multi-view data. Multi-view learning is a machine learning research field that models on the knowledge from multiple views. Many research works have verified that the utilization of multiple views can significantly improve the prediction effect of the model, so that a lot of models and algorithms are proposed. Existing multi-view learning models mainly follow the consensus principle and the complementarity principle. A typical SVM-based multi-view learning model, SVM-2K, extends support vector machine (SVM) for multi-view learning by using the distance minimization version of Kernel Canonical Correlation Analysis (KCCA). However, SVM-2K cannot fully unleash the power of the complementary information among different feature views. In this paper, we propose a new margin transfer-based multi-view support vector machine model, termed as M^2SVM. This brings a new model that incorporates both principles for multi-view learning. Furthermore, we theoretically analyze the performance of M^2SVM from the viewpoint of the consensus principle. Comparisons with SVM-2K reveal that M^2SVM is more flexible and favorable than SVM-2K. Experimental results on 50 binary data sets demonstrate the effectiveness of the proposed method.