Semi-supervised learning of local structured output predictors

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• 作者：Xin Du ^{xindu.njtu@gmail.com}Author Vitae
• 关键词：Machine learning ; Structured output ; Semi-supervised learning ; Local linear regression ; Gradient descent
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：12 January 2017
• 年：2017
• 卷：220
• 期：Complete
• 页码：151-159
• 全文大小：1142 K
• 卷排序：220

文摘

In this paper, we study the problem of semi-supervised structured output prediction, which aims to learn predictors for structured outputs, such as sequences, tree nodes, vectors, etc., from a set of data points of both input–output pairs and single inputs without outputs. The traditional methods to solve this problem usually learn one single predictor for all the data points, and ignore the variety of the different data points. Different parts of the data set may have different local distributions and require different optimal local predictors. To overcome this disadvantage of existing methods, we propose to learn different local predictors for neighborhoods of different data points, and the missing structured outputs simultaneously. In the neighborhood of each data point, we proposed to learn a linear predictor by minimizing both the complexity of the predictor and the upper bound of the structured prediction loss. The minimization is conducted by gradient descent algorithms. Experiments over four benchmark data sets, including DDSM mammography medical images, SUN natural image data set, Cora research paper data set, and Spanish news wire article sentence data set, show the advantages of the proposed method.