Papers Spectral Graph Clustering
“Spectral Graph Clustering” 태그가 달린 논문 30편 · 필터 해제
Modular Training of Neural Networks aids Interpretability
An approach to improve neural network interpretability is via clusterability, i.e., splitting a model into disjoint clusters that can be studied independently. We define a measure for clusterability and show that pre-tra…
Graph ClusteringSpectral Graph ClusteringTwo 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. We present a scaling inspired by the normalized Laplacian (NL) for graphs th…
Clusteringdocument understandingGraph ClusteringSpectral Graph Clustering+2Random projection tree similarity metric for SpectralNet
SpectralNet is a graph clustering method that uses neural network to find an embedding that separates the data. So far it was only used with $k$-nn graphs, which are usually constructed using a distance metric (e.g., Euc…
ClusteringGraph ClusteringGraph Embeddinggraph partitioning+1A parameter-free graph reduction for spectral clustering and SpectralNet
Graph-based clustering methods like spectral clustering and SpectralNet are very efficient in detecting clusters of non-convex shapes. Unlike the popular $k$-means, graph-based clustering methods do not assume that each …
ClusteringGraph ClusteringGraph Embeddinggraph partitioning+1Approximate spectral clustering density-based similarity for noisy datasets
Approximate spectral clustering (ASC) was developed to overcome heavy computational demands of spectral clustering (SC). It maintains SC ability in predicting non-convex clusters. Since it involves a preprocessing step, …
ClusteringGraph ClusteringGraph Embeddinggraph partitioning+2Approximate 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. Unfortunately, with a computational complexity of $O(n^3)$, it was i…
ClusteringGraph ClusteringGraph Embeddinggraph partitioning+1Refining a $k$-nearest neighbor graph for a computationally efficient spectral clustering
Spectral clustering became a popular choice for data clustering for its ability of uncovering clusters of different shapes. However, it is not always preferable over other clustering methods due to its computational dema…
ClusteringComputational EfficiencyGraph ClusteringGraph Embedding+3Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time
We pose video object segmentation as spectral graph clustering in space and time, with one graph node for each pixel and edges forming local space-time neighborhoods. We claim that the strongest cluster in this video gra…
ClusteringGPUGraph ClusteringObject+6Minimalistic Unsupervised Learning with the Sparse Manifold Transform
We describe a minimalistic and interpretable method for unsupervised learning, without resorting to data augmentation, hyperparameter tuning, or other engineering designs, that achieves performance close to the SOTA SSL …
Self-Supervised LearningSparse Representation-based ClassificationSpectral Graph ClusteringUnsupervised Image Classification+1Spectral 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 t…
ClusteringGraph ClusteringSpectral Graph ClusteringLearning 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 vi…
Graph ClusteringObject DiscoveryRetrievalSpectral Graph Clustering+1Latent structure blockmodels for Bayesian spectral graph clustering
Spectral embedding of network adjacency matrices often produces node representations living approximately around low-dimensional submanifold structures. In particular, hidden substructure is expected to arise when the gr…
ClusteringGraph ClusteringSpectral Graph ClusteringRefining a -nearest neighbor graph for a computationally efficient spectral clustering
Spectral clustering became a popular choice for data clustering for its ability of uncovering clusters of different shapes. However, it is not always preferable over other clustering methods due to its computational dema…
ClusteringComputational EfficiencyGraph ClusteringGraph Embedding+3Multiway $p$-spectral graph cuts on Grassmann manifolds
Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background. We present a novel direct multiway spect…
ClusteringGraph ClusteringSpectral Graph ClusteringEnsemble 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-associat…
ClusteringClustering EnsembleSpectral Graph ClusteringSimultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is ea…
ClusteringGraph ClusteringModel SelectionSpectral Graph Clustering+1An Internal Validity Index Based on Density-Involved Distance
It is crucial to evaluate the quality of clustering results in cluster analysis. Although many cluster validity indices (CVIs) have been proposed in the literature, they have some limitations when dealing with non-spheri…
ClusteringClustering Algorithms EvaluationClustering EnsembleFace Clustering+3Computing Nonlinear Eigenfunctions via Gradient Flow Extinction
In this work we investigate the computation of nonlinear eigenfunctions via the extinction profiles of gradient flows. We analyze a scheme that recursively subtracts such eigenfunctions from given data and show that this…
BIG-bench Machine LearningClusteringGraph ClusteringSpectral Graph ClusteringCLEAR: 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. Multi-way data association methods provide a mechanism to improve alignment ac…
ClusteringGraph ClusteringSpectral Graph ClusteringOn a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering - clustering the vertices of a graph based on their spectral embedding - is commonly…
ClusteringDiffusion MRIGraph ClusteringSpectral Graph Clustering+1