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Learning Kernel Expansions for Image Classification This project is no longer active.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 (ftorre@cs.cmu.edu) Mailing address: Carnegie Mellon University Robotics Institute 211 Smith Hall Pittsburgh, PA 15213 Associated center: VASC

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Project Description

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.

Past members

	Name	Title	Email Address
	Fernando De la Torre Frade	Research Scientist	ftorre@cs.cmu.edu
	Oriol Vinyals	Visiting Scholar

Publications

Note: This list may not be comprehensive. It contains only those publications in the RI publications database. Entries are listed in reverse chronological order.

• Learning Kernel Expansions for Image Classification
  F. De la Torre Frade and O. Vinyals
  IEEE Conference on Computer Vision and Pattern Recognition, June, 2007. [Abstract]
  Download: pdf [830 KB] copyrighted