# Localized Multiple Kernel Learning---A Convex Approach

arXiv: https://arxiv.org/abs/1506.04364 · HTML: https://mlsanity.com/paper/1506.04364/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2015.

## Built on by (most cited first, 1 of 1)

- [Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels](https://mlsanity.com/paper/2204.10675/) (2022)

## Abstract (arXiv)

We propose a localized approach to multiple kernel learning that can be formulated as a convex optimization problem over a given cluster structure. For which we obtain generalization error guarantees and derive an optimization algorithm based on the Fenchel dual representation. Experiments on real-world datasets from the application domains of computational biology and computer vision show that convex localized multiple kernel learning can achieve higher prediction accuracies than its global and non-convex local counterparts.