L2 Regularization for Learning Kernels

Understand

The choice of the kernel is critical to the success of many learning algorithms but it is typically left to the user.

• Instead, the training data can be used to learn the kernel by selecting it out of a given family, such as that of non-negative linear combinations of p base kernels, constrained by a trace or L1 regularization.
• This paper studies the problem of learning kernels with the same family of kernels but with an L2 regularization instead, and for regression problems.
• We analyze the problem of learning kernels with ridge regression.