Everything about Diffusion map
Geometric algorithm
Diffusion maps is a dimensionality reduction or feature extraction algorithm introduced by Coifman and Lafon which computes a family of embeddings of a data set into Euclidean space whose coordinates can be computed from the eigenvectors and eigenvalues of a diffusion operator on the data. The Euclidean distance between points in the embedded space is equal to the "diffusion distance" between probability distributions centered at those points. 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 discovering the underlying manifold that the data has been sampled from. By integrating local similarities at different scales, diffusion maps give a global description of the data-set. Compared with other methods, the diffusion map algorithm is robust to noise perturbation and computationally inexpensive. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Diffusion mapA 5-minute orientation
- Skim a recent paper: Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A reviewInfluential work since 2022
- Pick up a classic book: An Atlas of World AffairsThe most-reprinted book on the subject
- Read the landmark paper: Determination of reaction coordinates via locally scaled diffusion mapCited 249 times
- Go sideways: Nonlinear dimensionality reductionA closely related topic
- Go sideways: Spectral clusteringA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Determination of reaction coordinates via locally scaled diffusion map
Road Map For Diffusion Of Innovation In Health Care
Extended substitution–diffusion based image cipher using chaotic standard map
Stochastic diffusion in the standard map
Polymer reversal rate calculated via locally scaled diffusion map
Nonstandard Diffusion Properties of the Standard Map
Anomalous Diffusion Due to Accelerator Modes in the Standard Map
Diffusion in the standard map
🆕 Influential since 2022
Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review
RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction
Image encryption using fission diffusion process and a new hyperchaotic map
Compositional Text-to-Image Synthesis with Attention Map Control of Diffusion Models
Denoising Diffusion Probabilistic Model for Radio Map Estimation in Generative Wireless Networks
📚 Books worth reading
An Atlas of World AffairsAndrew Boyd · 1960- 📕Regional pollution potential in the northwestern United StatesSue A. Ferguson · 2003
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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