# Landmark Alternating Diffusion

arXiv: https://arxiv.org/abs/2404.19649 · HTML: https://mlsanity.com/paper/2404.19649/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Deep Scattering Spectrum](https://mlsanity.com/paper/1304.6763/) (2013)
- [Nonparametric Canonical Correlation Analysis](https://mlsanity.com/paper/1511.04839/) (2015)
- [Spectral Convergence of the connection Laplacian from random samples](https://mlsanity.com/paper/1306.1587/) (2013)
- [Latent common manifold learning with alternating diffusion: analysis and applications](https://mlsanity.com/paper/1602.00078/) (2016)
- [Recovering Hidden Components in Multimodal Data with Composite Diffusion Operators](https://mlsanity.com/paper/1808.07312/) (2018)
- [Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators](https://mlsanity.com/paper/2201.08530/) (2022)
- [How do kernel-based sensor fusion algorithms behave under high dimensional noise?](https://mlsanity.com/paper/2111.10940/) (2021)
- [Multi-View Kernels for Low-Dimensional Modeling of Seismic Events](https://mlsanity.com/paper/1706.01750/) (2017)
- [Diffuse to fuse EEG spectra -- intrinsic geometry of sleep dynamics for classification](https://mlsanity.com/paper/1803.01710/) (2018)
- [Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection](https://mlsanity.com/paper/1604.02946/) (2016)
- [Explore intrinsic geometry of sleep dynamics and predict sleep stage by unsupervised learning techniques](https://mlsanity.com/paper/1905.04589/) (2019)

## Built on by (most cited first, 2 of 2)

- [Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators](https://mlsanity.com/paper/2405.12317/) (2024)
- [Accelerate Vector Diffusion Maps by Landmarks](https://mlsanity.com/paper/2603.21247/) (2026)

## Abstract (arXiv)

Alternating Diffusion (AD) is a commonly applied diffusion-based sensor fusion algorithm. While it has been successfully applied to various problems, its computational burden remains a limitation. Inspired by the landmark diffusion idea considered in the Robust and Scalable Embedding via Landmark Diffusion (ROSELAND), we propose a variation of AD, called Landmark AD (LAD), which captures the essence of AD while offering superior computational efficiency. We provide a series of theoretical analyses of LAD under the manifold setup and apply it to the automatic sleep stage annotation problem with two electroencephalogram channels to demonstrate its application.