基于流形学习的多示例回归算法

A Manifold Learning-Based Multi-Instance Regression Algorithm

多示例学习是一种新型机器学习框架,以往的研究主要集中在多示例分类上,最近多示例回归受到了国际机器学习界的关注.流形学习旨在获得非线性分布数据的内在结构,可以用于非线性降维.文中基于流形学习技术,提出了用于解决多示例回归问题的ManiMIL算法.该算法首先对训练包中的示例降维,利用降维结果出现坍缩的特性对多示例包进行预测.实验表明,ManiMIL算法比现有的多示例算法例如Citation-kNN等有更好的性能.

Multi-instance learning is regarded as a new learning framework. Previous researches mainly focus on multi instance classification. Recently, multi instance regression attracts the attention of the machine learning community. Manifold learning attempts to obtain the intrinsic structure of non-linearly distributed data, which can be used in non-linear dimensionality reduction （NLDR）. In this paper, a manifold learning-based multi-instance regression algorithm, ManiMIL, is proposed. ManiMIL performs NLDR on the instances in training bags, selects the most diverse dimension that NLDR brings and builds a classifier only on this dimension and then makes the prediction. Experimental results show that the performance of ManiMIL outperforms that of existing multi instance algorithms such as Citation-kNN.