Image/Document Clustering
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Benchmarks
pendigits
BA
JAFFE
Wine
australian
iris
pixraw10P
warpPIE10P
Most implemented
Robust Graph Learning from Noisy Data
Divide-and-conquer based Large-Scale Spectral Clustering
Ensemble Learning for Spectral Clustering
Deep Embedded SOM: Joint Representation Learning and Self-Organization
An Internal Validity Index Based on Density-Involved Distance
Scalable Spectral Clustering Using Random Binning Features
Papers
Divide-and-conquer based Large-Scale Spectral Clustering
Spectral clustering is one of the most popular clustering methods. However, how to balance the efficiency and effectiveness of the large-scale spectral clustering with limited computing resources has not been properly so…
ClusteringImage/Document ClusteringEnsemble Learning for Spectral Clustering
Ensemble clustering has attracted much attention in machine learning and data mining for the high performance in the task of clustering. Spectral clustering is one of the most popular clustering methods and has superior …
ClusteringEnsemble LearningImage/Document ClusteringDeep Embedded SOM: Joint Representation Learning and Self-Organization
In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map. Our mod…
ClusteringDimensionality ReductionImage/Document ClusteringRepresentation Learning+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+3Ultra-Scalable Spectral Clustering and Ensemble Clustering
This paper focuses on scalability and robustness of spectral clustering for extremely large-scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra-scalable spectral clustering (U-SPEC) an…
ClusteringImage/Document ClusteringRobust Graph Learning from Noisy Data
Learning graphs from data automatically has shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreli…
ClusteringGeneral Classificationgraph constructionGraph Learning+5