Action Respecting Embedding

Michael Bowling, Ali Ghodsi, and Dana Wilkinson. Action Respecting Embedding. In Proceedings of the Twenty-Second International Conference on Machine Learning (ICML), pp. 65–72, 2005.




Dimensionality reduction is the problem of finding a low-dimensional representation of high-dimensional input data. This paper examines the case where additional information is known about the data. In particular, we assume the data are given in a sequence with action labels associated with adjacent data points, such as might come from a mobile robot. The goal is a variation on dimensionality reduction, where the output should be a representation of the input data that is both low-dimensional and respects the actions (i.e., actions correspond to simple transformations in the output representation). We show how this variation can be solved with a semidefinite program. We evaluate the technique in a synthetic, robot-inspired domain, demonstrating qualitatively superior representations and quantitative improvements on a data prediction task.


  Title = "Action Respecting Embedding",
  Author = "Michael Bowling and Ali Ghodsi and Dana Wilkinson",
  Booktitle = "Proceedings of the Twenty-Second International Conference on Machine Learning (ICML)",
  Year = "2005",
  Pages = "65--72",
  AcceptRate = "27\%",
  AcceptNumbers = "134 of 492"

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