具有协同约束的共生迁移学习算法研究

Symbiosis Transfer Learning Method with Collaborative Constraints

传统迁移学习方法通常直接利用相关领域中的数据来辅助完成当前领域的学习任务,而忽略了领域间互相学习的能力.针对此类问题,提出了一种具有协同约束的共生迁移学习方法(Collaborative Constraints based Symbiosis Transfer Learning,CCSTL).在协同约束的基础上,引入共生迁移机制实现领域间的交替互动学习,进而实现源领域和目标领域间的知识迁移,从而提高受训分类器的分类性能.在模拟数据和真实数据集上的实验结果表明了新算法的有效性.

Transfer learning algorithms usually focus on reusing data of related domains to help solving the learning tasks in the target domain. However, these methods ignore the ability of mutual learning between domains. In this paper, a collaborative con- straint based symbiosis transfer learning method （CCSTL） is proposed. Symbiotic transfer mechanism is used to implement mutual learning among domains along with the collaborative constraint. With the help of the iterative opfimizations, the proposed method can realize knowledge transfer between the source and target domains. Experimental results on synthetic and real world datasets show the superior or comparable performance of the proposed algorithm compared with existing algorithms.