Efficient subset selection based on the Renyi entropy

Many machine learning algorithms require the summation of Gaussian kernel functions, an expensive operation if implemented straightforwardly. Several methods have been proposed to reduce the computational complexity of evaluating such sums, including tree and analysis based methods. These achieve varying speedups depending on the bandwidth, dimension, and prescribed error, making the choice between methods difficult for machine learning tasks. We provide an algorithm that combines tree methods with the Improved Fast Gauss Transform (IFGT). As originally proposed the IFGT suffers from two problems: (1) the Taylor series expansion does not perform well for very low bandwidths, and (2) parameter selection is not trivial and can drastically affect performance and ease of use. We address the first problem by employing a tree data structure, resulting in four evaluation methods whose performance varies based on the distribution of sources and targets and input parameters such as ...

• Type: Conference
• Year: 2009
• Where: ICCV
• Authors: Vlad I. Morariu1, Balaji V. Srinivasan, Vikas C. Raykar, Ramani Duraiswami, Larry S. Davis
• Keywords: Computer Vision, ICCV 2009, Renyi Entropy, Subset Selection