A Note on Spectral Clustering and SVD of Graph Data

09/27/2018
by   Ziwei Zhang, et al.
0

Spectral clustering and Singular Value Decomposition (SVD) are both widely used technique for analyzing graph data. In this note, I will present their connections using simple linear algebra, aiming to provide some in-depth understanding for future research.

READ FULL TEXT

page 1

page 2

page 3

research
07/29/2022

Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering

Large graphs commonly appear in social networks, knowledge graphs, recom...
research
02/16/2023

Singular Value Representation: A New Graph Perspective On Neural Networks

We introduce the Singular Value Representation (SVR), a new method to re...
research
04/24/2021

Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image

Hyperspectral image (HSI) clustering, which aims at dividing hyperspectr...
research
09/04/2019

Automatic Differentiation for Complex Valued SVD

In this note, we report the back propagation formula for complex valued ...
research
11/29/2012

SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges

Singular Value Decomposition (SVD) has recently emerged as a new paradig...
research
09/28/2015

Compressive spectral embedding: sidestepping the SVD

Spectral embedding based on the Singular Value Decomposition (SVD) is a ...
research
01/29/2019

Approximating Spectral Clustering via Sampling: a Review

Spectral clustering refers to a family of unsupervised learning algorith...

Please sign up or login with your details

Forgot password? Click here to reset