# Geometric Fusion via Joint Delay Embeddings

arXiv: https://arxiv.org/abs/2002.11201 · HTML: https://mlsanity.com/paper/2002.11201/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

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

- [Topological Simplification of Signals for Inference and Approximate Reconstruction](https://mlsanity.com/paper/2206.07486/) (2022)

## Abstract (arXiv)

We introduce geometric and topological methods to develop a new framework for fusing multi-sensor time series. This framework consists of two steps: (1) a joint delay embedding, which reconstructs a high-dimensional state space in which our sensors correspond to observation functions, and (2) a simple orthogonalization scheme, which accounts for tangencies between such observation functions, and produces a more diversified geometry on the embedding space. We conclude with some synthetic and real-world experiments demonstrating that our framework outperforms traditional metric fusion methods.