Results 51 to 60 of about 74,563 (305)
Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 In this article, we integrate the spatial-spectral information of hyperspectral image (HSI) samples into nonnegative matrix factorization (NMF) for affinity matrix learning to address the issue of HSI clustering.
Yao Qin +5 more
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Dealing with non-metric dissimilarities in fuzzy central clustering algorithms [PDF]
, 2008 Clustering is the problem of grouping objects on the basis of a similarity measure among them. Relational clustering methods can be employed when a feature-based representation of the objects is not available, and their description is given in terms of ...
Filippone, Maurizio +2 more
core +1 more source
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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Impact of regularization on spectral clustering [PDF]
2014 Information Theory and Applications Workshop (ITA), 2014 37 ...
Joseph, Antony, Yu, Bin
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IEEE Access, 2023 This study embarks on an in-depth analysis of the performance of various kernel functions, namely uniform, epanechnikov, triangular, and gaussian, in window-based and spectral clustering-based models.
Petr Silhavy, Radek Silhavy
doaj +1 more source
Flow-Based Clustering and Spectral Clustering: A Comparison
2021 55th Asilomar Conference on Signals, Systems, and Computers, 2021 We propose and study a novel graph clustering method for data with an intrinsic network structure. Similar to spectral clustering, we exploit an intrinsic network structure of data to construct Euclidean feature vectors. These feature vectors can then be fed into basic clustering methods such as k-means or Gaussian mixture model (GMM) based soft ...
Sarcheshmehpour, Y. +5 more
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Multidimensional partitioning and bi-partitioning : analysis and application to gene expression datasets [PDF]
, 2008 Eigenvectors and, more generally, singular vectors, have proved to be useful tools for data mining and dimension reduction. Spectral clustering and reordering algorithms have been designed and implemented in many disciplines, and they can be motivated ...
Gabriela Kalna +8 more
core +1 more source
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 clustering with imbalanced data [PDF]
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014 Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and unbalanced clusters these algorithms can lead to poor performance on well-known graphs such as $k$-NN, full-RBF, $ε$-graphs. This is because the objectives such as Ratio-Cut
Jing Qian, Venkatesh Saligrama
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Average Sensitivity of Spectral Clustering [PDF]
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020 KDD ...
Peng, P., Yoshida, Y.
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