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Learning Kernel Expansions for Image Classification
This project is no longer active.
Head: Fernando De la Torre Frade
Contact: Fernando De la Torre Frade
Mailing address:
Carnegie Mellon University
Robotics Institute
211 Smith Hall
Pittsburgh, PA 15213
Associated center(s) / consortia:
 Vision and Autonomous Systems Center (VASC)
Kernel machines (e.g. SVM, KLDA) have shown state-of-the-art performance in several visual classification tasks. The classification performance of kernel machines greatly depends on the choice of kernels and its parameters. In this project, we propose a method to search over the space of parameterized kernels using a gradient-based method. Our method effectively learns a non-linear representation of the data useful for classification and simultaneously performs dimensionality reduction.