# A Multiple Parameter Linear Scale-Space for one dimensional Signal Classification

arXiv: https://arxiv.org/abs/2305.13350 · HTML: https://mlsanity.com/paper/2305.13350/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 1 of 1)

- [Sampling the flow of a bandlimited function](https://mlsanity.com/paper/2004.14032/) (2020)

## Abstract (arXiv)

In this article we construct a maximal set of kernels for a multi-parameter linear scale-space that allow us to construct trees for classification and recognition of one-dimensional continuous signals similar the Gaussian linear scale-space approach. Fourier transform formulas are provided and used for quick and efficient computations. A number of useful properties of the maximal set of kernels are derived. We also strengthen and generalize some previous results on the classification of Gaussian kernels. Finally, a new topologically invariant method of constructing trees is introduced.