Action respecting embedding

Dimensionality reduction is the problem of finding a low-dimensional representation of highdimensional 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.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Michael H. Bowling, Ali Ghodsi, Dana F. Wilkinson
• Keywords: Adjacent Data Points, Data Prediction Task, Dimensionality Reduction, ICML 2005, Machine Learning