Structured Output Prediction Using Privileged Information

Abstract

We study the problem of structured output prediction. Some methods such as structured output support vector machines (SSVM) and conditional random fields (CRFs) are state-of-The-Art in dealing with the structured data. However, these classical methods have some limits in scalability because of high memory requirements and slow training speed. Recently, the method joint kernel support estimation (JKSE) has been proposed based on one-class SVM which can be trained efficiently. However, JKSE is not as powerful as those classical methods from the point of prediction performance. To improve the performance of JKSE, we introduce privileged information into it. Learning using privileged information (LUPI) is an advanced machine learning paradigm by taking advantage of some elements of human teaching that are only available at the training stage, not at testing. Motivated by the LUPI, we propose three new models based on JKSE by considering three forms of privileged information. The resulting optimization problems are convex quadratic programming that can be easily solved. Our new models not only persist the advantage of JKSE but also improve its performance. The experimental results show the superiority of new models over the JKSE when solving object detection and multi-class classification problems.