Results 41 to 50 of about 1,461,923 (295)
Large scale clustering of protein sequences with FORCE - a layout based heuristic for weighted cluster editing [PDF]
, 2007 Wittkop T, Baumbach J, Lobo FP, Rahmann S. Large scale clustering of protein sequences with FORCE - a layout based heuristic for weighted cluster editing. BMC Bioinformatics.
Wittkop, Tobias +11 more
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Unsupervised segmentation of mitochondria using model-based spectral clustering [PDF]
, 2013 Segmentation of mitochondria in microscopic images represents a significant challenge that is motivated by the wide morphological and structural variations that are characteristic for this category of membrane enclosed sub cellular organelles. To address
Ghita, Ovidiu +2 more
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Compressive Spectral Clustering
CoRR, 2016 Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features to separate objects into k classes.
Tremblay, Nicolas +3 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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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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Patent clustering using spectral clustering.
, 2020 Patent clustering using spectral clustering.
Inchae Park (8407338) +2 more
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KNN-SC: Novel Spectral Clustering Algorithm Using k-Nearest Neighbors
IEEE Access, 2021 Spectral clustering is a well-known graph-theoretic clustering algorithm. Although spectral clustering has several desirable advantages (such as the capability of discovering non-convex clusters and applicability to any data type), it often leads to ...
Jeong-Hun Kim +4 more
doaj +1 more source
Transfer Spectral Clustering [PDF]
, 2012 Transferring knowledge from auxiliary datasets has been proved useful in machine learning tasks. Its adoption in clustering however is still limited. Despite of its superior performance, spectral clustering has not yet been incorporated with knowledge transfer or transfer learning.
Wenhao Jiang, Fu-Lai Chung
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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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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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