A Manifold Learning-Based Multi-Instance Regression Algorithm
Zhan De · Chinese Journal of Computers · 2006
Multi-instance learning is regarded as a new learning framework.Previous researches mainly focus on multiinstance 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.