基于最大间隔和流形假设的半监督学习算法

Semi-supervised Learning Algorithm Based on Maximum Margin and Manifold Hypothesis

半监督学习是一种介于监督学习和无监督学习之间的弱监督学习模式,其在学习过程中将少量标记示例和大量未标记示例结合起来构建模型,以期取得比监督学习仅使用标记示例更高的学习精度。在该学习模式下,文中提出了一种将最大间隔准则和示例空间的流形假设思想相结合的半监督学习算法。该算法在利用示例流形结构估计未标记示例标记置信度的同时利用最大间隔准则构建分类模型,并采用交叉优化方法以迭代的方式交替地求解分类模型参数和标记置信度。在12个UCI数据集和4个由MNIST手写数字集生成的数据集上的实验结果表明,采用半监督直推学习方式,该算法的性能优于其他对比算法的情况为60.5%;采用半监督归纳学习方式,该算法的性能优于其他对比算法的情况为42.6%。

Semi-supervised learning is a weakly supervised learning pattern between supervised learning and unsupervised lear-ning.It combines a small number of labeled instances with a large number of unlabeled instances to build a model during the process of learning,hoping to achieve better learning accuracy than supervised learning using only labeled instances.In this lear-ning pattern,this paper proposes a semi-supervised learning algorithm that combines the maximum margin with manifold hypo-thesis of the instance space.The algorithm utilizes the manifold structure of instances to estimate the labeling confidence over unlabeled instances,at the same time utilizes the maximum margin to derive the classification model.And alternating optimization is adopted to address the quadratic programming problem of the model parameters and the labeling confidence in an iterative manner.On 12 UCI datasets and 4 datasets generated by the MNIST database of handwritten digits,in semi-supervised transductive learning,the proposed algorithm’s performance outperforms the comparison algorithms for 60.5%of the configurations in semi-supervised inductive learning,the proposed algorithm’s performance outperforms the comparison algorithms for 42.6%of the configurations.