# Solving ℓp!-norm regularization with tensor kernels

arXiv: https://arxiv.org/abs/1707.05609 · HTML: https://mlsanity.com/paper/1707.05609/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2017.

## Built on by (most cited first, 3 of 3)

- [When big data actually are low-rank, or entrywise approximation of certain function-generated matrices](https://mlsanity.com/paper/2407.03250/) (2024)
- [Locality Sensitive Hashing for p-Exponential Kernels with Applications to Density Estimation](https://mlsanity.com/paper/2610.05174/) (2026)
- [A Kernelizable Primal-Dual Formulation of the Multilinear Singular Value Decomposition](https://mlsanity.com/paper/2410.10504/) (2024)

## Abstract (arXiv)

In this paper, we discuss how a suitable family of tensor kernels can be used to efficiently solve nonparametric extensions of ℓp regularized learning methods. Our main contribution is proposing a fast dual algorithm, and showing that it allows to solve the problem efficiently. Our results contrast recent findings suggesting kernel methods cannot be extended beyond Hilbert setting. Numerical experiments confirm the effectiveness of the method.