Results 241 to 250 of about 64,517 (265)

Spectral clustering with the probabilistic cluster kernel

Neurocomputing, 2015
Abstract 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, Robert Jenssen, Luis Gómez-Chova, Gustavo Camps-Valls   +3 more
openaire   +1 more source

Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering

IEEE Transactions on Neural Networks, 2011
Spectral 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.
Feiping Nie 0001, Zinan Zeng, Ivor W. Tsang, Dong Xu 0001, Changshui Zhang   +4 more
openaire   +2 more sources

Spectral Clustering on Multiple Manifolds

IEEE Transactions on Neural Networks, 2011
Spectral 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, Yuan Jiang 0001, Yi Wu 0003, Zhi-Hua Zhou   +3 more
openaire   +2 more sources

Linear Spectral Clustering Superpixel

IEEE Transactions on Image Processing, 2017
In 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, Zhengqin Li, Bo Huang   +2 more
openaire   +2 more sources

Discrete Nonnegative Spectral Clustering

IEEE Transactions on Knowledge and Data Engineering, 2017
Spectral clustering has been playing a vital role in various research areas. Most traditional spectral clustering algorithms comprise two independent stages (e.g., first learning continuous labels and then rounding the learned labels into discrete ones), which may cause unpredictable deviation of resultant cluster labels from genuine ones, thereby ...
Yang, Yang, Shen, Fumin, Huang, Zi, Shen, Heng Tao, Li, Xuelong, Shen, HT   +5 more
openaire   +4 more sources

Limits of Spectral Clustering.

2005
An 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., Bousquet, O., Belkin, M.   +2 more
openaire   +2 more sources

Unified One-Step Multi-View Spectral Clustering

IEEE Transactions on Knowledge and Data Engineering, 2023
Xinwang Liu, Chang Tang, Zhenglai Li
exaly  

Large Graph Clustering With Simultaneous Spectral Embedding and Discretization

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Zhen Wang, Zhaoqing Li, Rong Wang
exaly  

Spectral Contrastive Clustering

Pattern Recognition
Jerome Williams, Antonio Robles-Kelly
openaire   +1 more source

Fast Multiview Clustering With Spectral Embedding

IEEE Transactions on Image Processing, 2022
Ben Yang, Feiping Nie, Xuetao Zhang
exaly