Mercer kernel-based clustering in feature space

By Mark A. Girolami. IEEE Trans. Neural Networks, 2002

940 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7da07348d6fcb2b292da76285f3a371b02b8296b

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Citations in the corpus

A novel kernelized fuzzy C-means algorithm with application in medical image segmentation (2004, methodology): “This trick can also be used in clustering, as shown in support vector clustering [18] and kernel (fuzzy) c-means algorithms [19,20].”
Learning via Hilbert Space Embedding of Distributions (2007, methodology): “A popular clustering algorithm is k-means [ 91], which can be kernelized straightforwardly [116, 55].”
A survey of kernel and spectral methods for clustering (2008, methodology): “(39) The minimization technique used by Girolami is deterministic annealing [76] which is a stochastic method for optimization.”
Secrets of GrabCut and Kernel K-Means (2015, methodology): “In machine learning, kernel K-means (kKM) is a well established data clustering technique [34, 25, 13, 11, 9, 15], which can identify complex structures that are non-linearly separable in input space.”
Kernel Methods and Machine Learning (2014, background): “In the kernel approach, the pair-wise similarity between two training samples, say x and y, is characterized by the kernel function K(x, y) [78, 189].”
Weighted Graph Cuts without Eigenvectors A Multilevel Approach (2007, methodology): “Kernel-based clustering using s pectral methods was discussed in [27], [28]; the spectral algorithm s discussed in [28] correspond to optimizing ratio associati on and ratio cut.”
A novel kernel method for clustering (2005, methodology): “While powerful kernel methods have been proposed for supervised classification and regression problems, the development of effective kernel method for clustering, aside for a few tentative solutions [9], [18], is still an open problem.”
A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification (2011, methodology): “In [39], kernel -means [43]–[45] was used to cluster the samples selected by MCLU and select diverse batches.”
Multiple Kernel Fuzzy Clustering (2012, methodology): “Thus, kernel-based clustering has been proposed to perform clustering in a typically higher dimensional feature space spanned by embedding maps and corresponding kernel functions [10].”
Kernel k-means: spectral clustering and normalized cuts (2004, methodology): “In [7], the objective function was recast as a trace maximization, but they developed an EM-style algorithm to solve the kernel k -means problem.”
A survey of dimensionality reduction techniques (2014, background): “Some authors [Girolami2002, Dhillon2004, Yu2012] have proposed to use a non ‐ linear embedding of the input vectors   Φ x into a higher dimensional space (dimensionality expansion, instead of reduction), and then perform the vector quantization in this higher dimensional space (this kind of…”
Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery (2005, methodology): “In [21], a kernel-based clustering algorithm was proposed and in [17] kernels were used as generalized dissimilarity measures for classification.”
Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering (2011, methodology): “Moreover, clustering methods such as kernel-based clustering [7], [8], spectral clustering (SC) [9]–[13] and support vector clustering [14] have been developed to capture nonlinear cluster structures.”
Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure (2004, methodology): “Therefore , the inner product form in (1b) leads us to applying the kernel methods to cluster complex data [21], [22].”