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Learning to Predict Where People Look with Tensor-Based Multi-view Learning

  • Kitsuchart PasupaEmail author
  • Sandor Szedmak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)

Abstract

Eye movements data collection is very expensive and laborious. Moreover, there are usually missing values. Assuming that we are collecting eye movements data on a set of images from different users (views). There is a possibility that we are not able to collect eye movements of all users on all images. One or more views are not represented in the image. We assume that the relationships among the views can be learnt from the complete items. The task is then to reproduce the missing part of the incomplete items from the relationships derived from the complete items and the known part of these items. Using the properties of tensor algebra we show that this problem can be formulated consistently as a regression type learning task. Furthermore, there is a maximum margin based optimisation framework where this problem can be solved in a tractable way. This problem is similar to learning to predict where human look. The proposed algorithm is proved to be more effective than well-known saliency detection techniques.

Keywords

Multi-view learning Missing data Tensor algebra One rank tensor approximation Maximum margin learning Eye movements 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Information TechnologyKing Mongkut’s Institute of Technology LadkrabangBangkokThailand
  2. 2.Institute of Computer ScienceUniversity of InnsbruckInnsbruckAustria

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