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.
@InProceedings(05icml-are, 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" )