# Reduced Kernel Dictionary Learning

arXiv: https://arxiv.org/abs/2307.08798 · HTML: https://mlsanity.com/paper/2307.08798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 2 of 2)

- [Linearized Kernel Dictionary Learning](https://mlsanity.com/paper/1509.05634/) (2015)
- [Multiple Kernel Sparse Representations for Supervised and Unsupervised Learning](https://mlsanity.com/paper/1303.0582/) (2013)

## Abstract (arXiv)

In this paper we present new algorithms for training reduced-size nonlinear representations in the Kernel Dictionary Learning (KDL) problem. Standard KDL has the drawback of a large size of the kernel matrix when the data set is large. There are several ways of reducing the kernel size, notably Nystr"om sampling. We propose here a method more in the spirit of dictionary learning, where the kernel vectors are obtained with a trained sparse representation of the input signals. Moreover, we optimize directly the kernel vectors in the KDL process, using gradient descent steps. We show with three data sets that our algorithms are able to provide better representations, despite using a small number of kernel vectors, and also decrease the execution time with respect to KDL.