Spectral Graph Clustering
15 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Spectral Graph Clustering
Latest papers
A parameter-free graph reduction for spectral clustering and SpectralNet
mashaan14/SC-parameter-free
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We introduce a graph reduction method that does not require any parameters.
Random projection tree similarity metric for SpectralNet
mashaan14/RPTree-SpectralNet
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Our experiments revealed that SpectralNet produces better clustering accuracy using rpTree similarity metric compared to $k$-nn graph with a distance metric.
Refining a $k$-nearest neighbor graph for a computationally efficient spectral clustering
mashaan14/Spectral-Clustering
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We proposed a refined version of $k$-nearest neighbor graph, in which we keep data points and aggressively reduce number of edges for computational efficiency.
Approximate spectral clustering with eigenvector selection and self-tuned $k$
The recently emerged spectral clustering surpasses conventional clustering methods by detecting clusters of any shape without the convexity assumption.
Approximate spectral clustering density-based similarity for noisy datasets
Also, CONN could be tricked by noisy density between clusters.
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.
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.
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.
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.
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
The second contribution is a simultaneous model selection framework.
