Spectral Graph Clustering
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Spectral Graph Clustering
Most implemented papers
Symmetric Nonnegative Matrix Factorization for Graph Clustering
Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).
Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering
One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters.
CLEAR: A Consistent Lifting, Embedding, and Alignment Rectification Algorithm for Multi-View Data Association
Many robotics applications require alignment and fusion of observations obtained at multiple views to form a global model of the environment.
An Internal Validity Index Based on Density-Involved Distance
One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters.
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
The second contribution is a simultaneous model selection framework.
Ensemble clustering based on evidence extracted from the co-association matrix
The evidence accumulation model is an approach for collecting the information of base partitions in a clustering ensemble method, and can be viewed as a kernel transformation from the original data space to a co-association matrix.
Refining a -nearest neighbor graph for a computationally efficient spectral clustering
mashaan14/Spectral-Clustering
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Pattern Recognition 2021
We proposed a refined version of -nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency.
Latent structure blockmodels for Bayesian spectral graph clustering
Furthermore, the presence of communities within the network might generate community-specific submanifold structures in the embedding, but this is not explicitly accounted for in most statistical models for networks.
Learning Co-segmentation by Segment Swapping for Retrieval and Discovery
The goal of this work is to efficiently identify visually similar patterns in images, e. g. identifying an artwork detail copied between an engraving and an oil painting, or recognizing parts of a night-time photograph visible in its daytime counterpart.
