Results 31 to 40 of about 1,461,923 (295)
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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Sparse-reduced computation for large-scale spectral clustering [PDF]
, 2016 Clustering is a fundamental task in machine learning and data analysis. A large number of clustering algorithms has been developed over the past decades.
Baumann, Philipp, Philipp Baumann
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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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Parallel Spectral Clustering [PDF]
, 2008 Spectral clustering algorithm has been shown to be more effective in finding clusters than most traditional algorithms. However, spectral clustering suffers from a scalability problem in both memory use and computational time when a dataset size is large. To perform clustering on large datasets, we propose to parallelize both memory use and computation
Yangqiu Song +4 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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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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Spectral Clustering of Mixed-Type Data
Stats, 2021 Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups.
Felix Mbuga, Cristina Tortora
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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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Active Spectral Clustering [PDF]
2010 IEEE International Conference on Data Mining, 2010 The technique of spectral clustering is widely used to segment a range of data from graphs to images. Our work marks a natural progression of spectral clustering from the original passive unsupervised formulation to our active semi-supervised formulation. We follow the widely used area of constrained clustering and allow supervision in the form of pair
Xiang Wang 0001, Ian Davidson
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