Paper
9 September 2019 Metrics of graph Laplacian eigenvectors
Author Affiliations +
Abstract
The application of graph Laplacian eigenvectors has been quite popular in the graph signal processing field: one can use them as ingredients to design smooth multiscale basis. Our long-term goal is to study and understand the dual geometry of graph Laplacian eigenvectors. In order to do that, it is necessary to define a certain metric to measure the behavioral differences between each pair of the eigenvectors. Saito (2018) considered the ramified optimal transportation (ROT) cost between the square of the eigenvectors as such a metric. Clonginger and Steinerberger (2018) proposed a way to measure the affinity (or ‘similarity’) between the eigenvectors based on their Hadamard (HAD) product. In this article, we propose a simplified ROT metric that is more computational efficient and introduce two more ways to define the distance between the eigenvectors, i.e., the time-stepping diffusion (TSD) metric and the difference of absolute gradient (DAG) pseudometric. The TSD metric measures the cost of “flattening” the initial graph signal via diffusion process up to certain time, hence it can be viewed as a time-dependent version of the ROT metric. The DAG pseudometric is the l 2 -distance between the feature vectors derived from the eigenvectors, in particular, the absolute gradients of the eigenvectors. We then compare the performance of ROT, HAD and the two new “metrics” on different kinds of graphs. Finally, we investigate their relationship as well as their pros and cons.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haotian Li and Naoki Saito "Metrics of graph Laplacian eigenvectors", Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111381K (9 September 2019); https://doi.org/10.1117/12.2528644
Lens.org Logo
CITATIONS
Cited by 3 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Diffusion

Signal processing

Vector spaces

Fourier transforms

Matrices

Wavelet transforms

Wavelets

RELATED CONTENT

Wavelets and adaptive signal processing
Proceedings of SPIE (December 01 1991)
Optimality in the design of overcomplete decompositions
Proceedings of SPIE (September 04 2009)
New discrete unitary Haar-type heap transforms
Proceedings of SPIE (September 20 2007)
Discrete unitary transforms generated by moving waves
Proceedings of SPIE (September 20 2007)
Fast approximate Fourier transform via wavelets transform
Proceedings of SPIE (October 23 1996)
Wavelets: what kind of signal processing is that?
Proceedings of SPIE (June 07 1996)

Back to Top