Results 31 to 40 of about 64,517 (265)
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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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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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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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
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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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Impact of regularization on spectral clustering [PDF]
2014 Information Theory and Applications Workshop (ITA), 2014 37 ...
Joseph, Antony, Yu, Bin
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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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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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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
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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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