Semi-supervised Learning Algorithm Based on Maximum Margin and Manifold Hypothesis
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 un-labeled 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 man-ner.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.
万方数据
维普
