Results 271 to 280 of about 1,461,923 (295)
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On clusterings-good, bad and spectral
Proceedings 41st Annual Symposium on Foundations of Computer Science, 2002We motivate and develop a natural bicriteria measure for assessing the quality of a clustering that avoids the drawbacks of existing measures. A simple recursive heuristic is shown to have poly-logarithmic worst-case guarantees under the new measure. The main result of the article is the analysis of a popular spectral
Ravi Kannan +2 more
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Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering
IEEE Transactions on Neural Networks, 2011Spectral clustering (SC) methods have been successfully applied to many real-world applications. The success of these SC methods is largely based on the manifold assumption, namely, that two nearby data points in the high-density region of a low-dimensional data manifold have the same cluster label.
Ivor Wai-Hung Tsang +2 more
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2003
In this paper we explore how to use spectral methods for embedding and clustering unweighted graphs. We use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties.
Bin Luo 0001 +2 more
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In this paper we explore how to use spectral methods for embedding and clustering unweighted graphs. We use the leading eigenvectors of the graph adjacency matrix to define eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties.
Bin Luo 0001 +2 more
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2008
In this paper, we propose a novel spectral clustering algorithm called: Locality Spectral Clustering ( Lsc ) which assumes that each data point can be linearly reconstructed from its local neighborhoods. The Lsc algorithm firstly try to learn a smooth enough manifold structure on the data manifold and then computes the eigenvectors on the smooth ...
Yun-Chao Gong, Chuanliang Chen
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In this paper, we propose a novel spectral clustering algorithm called: Locality Spectral Clustering ( Lsc ) which assumes that each data point can be linearly reconstructed from its local neighborhoods. The Lsc algorithm firstly try to learn a smooth enough manifold structure on the data manifold and then computes the eigenvectors on the smooth ...
Yun-Chao Gong, Chuanliang Chen
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Spectral clustering with the probabilistic cluster kernel
Neurocomputing, 2015Abstract This letter introduces a probabilistic cluster kernel for data clustering. The proposed kernel is computed with the composition of dot products between the posterior probabilities obtained via GMM clustering. The kernel is directly learned from the data, is parameter-free, and captures the data manifold structure at different scales.
Emma Izquierdo-Verdiguier +3 more
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Spectral Clustering on Multiple Manifolds
IEEE Transactions on Neural Networks, 2011Spectral clustering (SC) is a large family of grouping methods that partition data using eigenvectors of an affinity matrix derived from the data. Though SC methods have been successfully applied to a large number of challenging clustering scenarios, it is noteworthy that they will fail when there are significant intersections among different clusters.
Yong Wang +3 more
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Linear Spectral Clustering Superpixel
IEEE Transactions on Image Processing, 2017In this paper, we present a superpixel segmentation algorithm called linear spectral clustering (LSC), which is capable of producing superpixels with both high boundary adherence and visual compactness for natural images with low computational costs. In LSC, a normalized cuts-based formulation of image segmentation is adopted using a distance metric ...
Jiansheng Chen 0001 +2 more
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Limits of Spectral Clustering.
2005An important aspect of clustering algorithms is whether the partitions constructed on finite samples converge to a useful clustering of the whole data space as the sample size increases. This paper investigates this question for normalized and unnormalized versions of the popular spectral clustering algorithm.
von Luxburg, U. +2 more
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Unified One-Step Multi-View Spectral Clustering
IEEE Transactions on Knowledge and Data Engineering, 2023Xinwang Liu, Chang Tang, Zhenglai Li
exaly
Large Graph Clustering With Simultaneous Spectral Embedding and Discretization
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021Zhen Wang, Zhaoqing Li, Rong Wang
exaly