NDLI: Manifold regularization in structured output space for semi-supervised structured output prediction

Content Provider	Springer Nature Link
Author	Jiang, Fei Jia, Lili Sheng, Xiaobao LeMieux, Riley
Copyright Year	2015
Abstract	Structured output prediction aims to learn a predictor to predict a structured output from a input data vector . The structured outputs include vector, tree, sequence, etc. We usually assume that we have a training set of input–output pairs to train the predictor. However, in many real-world applications, it is difficult to obtain the output for a input, and thus for many training input data points, the structured outputs are missing. In this paper, we discuss how to learn from a training set composed of some input–output pairs and some input data points without outputs. This problem is called semi-supervised structured output prediction. We propose a novel method for this problem by constructing a nearest neighbor graph from the input space to present the manifold structure and use it to regularize the structured output space directly. We define a slack structured output for each training data point and propose to predict it by learning a structured output predictor. The learning of both slack structured outputs and the predictor are unified within one single minimization problem. In this problem, we propose to minimize the structured loss between the slack structured outputs of neighboring data points and the prediction error measured by the structured loss. The problem is optimized by an iterative algorithm. Experiment results over three benchmark data sets show its advantage.
Starting Page	2605
Ending Page	2614
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	27
Issue Number	8
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2015-08-23
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Structured output prediction Structured loss Manifold regularization Neighborhood smoothness Gradient descent Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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