Results 21 to 30 of about 64,517 (265)
PolSAR Image Classification Method Based on Markov Discriminant Spectral Clustering
Leida xuebao, 2019 Due to the existing spectral clustering methods have low accuracy for PolSAR image classification, a Markov-based Discriminative Spectral Clustering(MDSC) method is proposed, which has the characteristics of low-rank and sparse decomposition.
ZHANG Xiangrong +4 more
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Scalable Spectral Clustering With Nyström Approximation: Practical and Theoretical Aspects
IEEE Open Journal of Signal Processing, 2020 Spectral clustering techniques are valuable tools in signal processing and machine learning for partitioning complex data sets. The effectiveness of spectral clustering stems from constructing a non-linear embedding based on creating a similarity graph ...
Farhad Pourkamali-Anaraki
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Density Gain-Rate Peaks for Spectral Clustering
IEEE Access, 2021 Clustering has been troubled by varying shapes of sample distributions, such as line and spiral shapes. Spectral clustering and density peak clustering are two feasible techniques to address this problem, and have attracted much attention from academic ...
Jiexing Liu, Chenggui Zhao
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Sensors, 2020 This paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis ...
Massimo Pacella, Gabriele Papadia
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Complexity, 2021 Abnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the ...
Yan Wu, Yonghong Chen, Wenhao Ling
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Newtonian Spectral Clustering [PDF]
, 2009 In this study we propose a systematic methodology for constructing a sparse affinity matrix to be used in an advantageous spectral clustering approach. Newton's equations of motion are employed to concentrate the data points around their cluster centers, using an appropriate potential.
Blekas, K. +2 more
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Spectral Embedded Deep Clustering
Entropy, 2019 We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space.
Yuichiro Wada +5 more
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Identifying cell types from single-cell data based on similarities and dissimilarities between cells
BMC Bioinformatics, 2021 Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research.
Yuanyuan Li +3 more
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Multiclass spectral clustering [PDF]
Proceedings Ninth IEEE International Conference on Computer Vision, 2003 We propose a principled account on multiclass spectral clustering. Given a discrete clustering formulation, we first solve a relaxed continuous optimization problem by eigen-decomposition. We clarify the role of eigenvectors as a generator of all optimal solutions through orthonormal transforms.
Stella X. Yu, Jianbo Shi
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Consistency of spectral clustering
The Annals of Statistics, 2008 Published in at http://dx.doi.org/10.1214/009053607000000640 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
von Luxburg, Ulrike +2 more
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