Preimage Problem in Kernel-Based Machine Learning

Kernel machines have gained considerable popularity during the last 15 years, making a breakthrough in nonlinear signal processing and machine learning, thanks to extraordinary advances. This increased interest is undoubtedly driven by the practical goal of being able to easily develop efficient nonlinear algorithms. The key principle behind this, known as the kernel trick, exploits the fact that a great number of data-processing techniques do not explicitly depend on the data itself but rather on a similarity measure between them, i.e., an inner product. © 2006 IEEE.