Spectral Graph Clustering
15 papers with code • 0 benchmarks • 0 datasets
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Latest papers with no code
Two to Five Truths in Non-Negative Matrix Factorization
In this paper, we explore the role of matrix scaling on a matrix of counts when building a topic model using non-negative matrix factorization.
Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time
Our motivation for a spectral space-time clustering approach, unique in video semantic segmentation literature, is that such clustering is dedicated to preserving object consistency over time, which we evaluate using our novel segmentation consistency measure.
Minimalistic Unsupervised Learning with the Sparse Manifold Transform
Though there remains a small performance gap between our simple constructive model and SOTA methods, the evidence points to this as a promising direction for achieving a principled and white-box approach to unsupervised learning.
Spectral Graph Clustering for Intentional Islanding Operations in Resilient Hybrid Energy Systems
Establishing cleaner energy generation therefore improving the sustainability of the power system is a crucial task in this century, and one of the key strategies being pursued is to shift the dependence on fossil fuel to renewable technologies such as wind, solar, and nuclear.
Multiway $p$-spectral graph cuts on Grassmann manifolds
We demonstrate the effectiveness and accuracy of our algorithm in various artificial test-cases.
Computing Nonlinear Eigenfunctions via Gradient Flow Extinction
In this work we investigate the computation of nonlinear eigenfunctions via the extinction profiles of gradient flows.
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings.
A Variational Image Segmentation Model based on Normalized Cut with Adaptive Similarity and Spatial Regularization
Thus, the segmentation results of the existing Ncut method are largely dependent on a pre-constructed similarity measure on the graph since this measure is usually given empirically by users.
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure
In order to have an in-depth theoretical understanding, in this manuscript, we investigate the graph degree in spectral graph clustering based and kernel based point of views and draw connections to a recent kernel method for the two sample problem.
Revisiting Spectral Graph Clustering with Generative Community Models
The presented method, SGC-GEN, not only considers the detection error caused by the corresponding model mismatch to a given graph, but also yields a theoretical guarantee on community detectability by analyzing Spectral Graph Clustering (SGC) under GENerative community models (GCMs).
