Different from linear dimensionality reduction methods such as principal component analysis (PCA), diffusion maps are part of the family of nonlinear dimensionality reduction methods which focus on …

A diffusion map, which maps coordinates between data and diffusion space, aims to re-organise data accord- ing to the diffusion metric. We exploit it for reducing dimensionality.

May 12, 2025 · Deep Diffusion Maps A novel implementation of diffusion maps using deep learning (neural networks). This repository contains the code for the experiments described in our paper.

Diffusion maps are a non-linear technique. Similar to taking a random walk on our data, where we jump between data points in feature space (see image below), we are analysing the connectivity of the data.

In this tutorial, we explore Diffusion Maps and PHATE, two powerful nonlinear dimensionality reduction techniques that excel in capturing the geometric structure of complex datasets, particularly in life …

Before presenting Diffusion Maps, we’ll introduce a few important notions. Given G = (V,E,W) we consider a random walk (with independent steps) on the vertices of V with transition probabilities:

May 9, 2025 · Dimensionality reduction methods make it possible to combat the so-called curse of dimensionality, visualize high-dimensional data and, in general, improve the efficiency of storing and …

Jul 1, 2006 · We show that eigenfunctions of Markov matrices can be used to construct coordinates called diffusion maps that generate efficient representations of complex geometric structures.

Diffusion maps are a non-linear dimensionality reduction technique that embeds the coordinates of each frame onto a lower-dimensional space, such that the distance between each frame in the lower …

2 Difusion Maps ptures geometrical properties of data. In order to do so, it utilizes weighted graphs, which encode lo al similarity between pairs of points. This local interaction then allows to obtain glo al …