基于迁移共享空间的分类新算法

A Classification Method Using Transferring Shared Subspace

为解决来自不同但相关领域的大量无标签数据和少量带标签数据的分类问题,首先构造一个联系源域到目标域的共享特征空间,并将该空间引入经典的支持向量机算法使其获得迁移能力,最终得到一种新的基于支持向量机的迁移共享空间的分类新算法,即迁移共享空间支持向量机.具体地,该方法以迁移学习理论为基础,结合分类器最大间隔原理,通过最大化无标签数据和带标签数据的联合概率分布来构建无标签数据和带标签数据的共享空间;为充分考虑少量带标签数据之数据分布,在其原始特征空间和共享空间组成的扩展空间中训练分类模型.相关实验结果验证了该迁移学习分类器的有效性.

Transfer learning algorithms have been proved efficiently in pattern classification filed.The characteristic of transfer learning is to better use one domain information to improve the classification performance in different but related domains.In order to effectively solve the classification problems with a few labeled and abundant unlabeled data coming from different but related domains,a new algorithm named transferring shared subspace support vector machine（TS3 VM）is proposed in this paper.Firstly a shared subspace used as the common knowledge between source domain and target domain is built and then classical support vector machine method is introduced to the subspace for the labeled data,therefore the resulting classification model has the ability of transfer learning.Specifically,using the theory of transfer learning and the principal of large margin classifier,the proposed algorithm constructs a shared subspace between two domains by maximizing the joint probability distribution of the labeled and unlabeled data.Meanwhile,in order to fully consider the distribution of the few labeled data,the classification model is trained in the augmented feature space consisting of the original space and the shared subspace.Experimental results confirm the efficiency of the proposed method.