Cooperative and penalized competitive learning with application to kernel-based clustering

Jia, Hong; Cheung, Yiu-ming; Liu, Jiming

doi:10.1016/j.patcog.2014.03.010

Cited by 24 publications

(12 citation statements)

References 14 publications

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“…Finally, Euclidean distances in the kernel space can be measured using dot products. Kernel k-Means provides a popular starting point for many state of the art clustering schemes [5,6,7,8]. A recent survey on kernel clustering methods can be found in [9], while [10] presents a comparative study which supports the superiority of kernel clustering methods, over more conventional clustering approaches.…”

Section: Introductionmentioning

confidence: 95%

A distributed framework for trimmed Kernel k-Means clustering

Tsapanos

Tefas

Nikolaidis

et al. 2015

Pattern Recognition

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Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data. Kernel k-Means is a state of the art clustering algorithm. However, in contrast to clustering algorithms that can work using only a limited percentage of the data at a time, Kernel k-Means is a global clustering algorithm. It requires the computation of the kernel matrix, which takes O(n 2 d) time and O(n 2 ) space in memory. As datasets grow larger, the application of Kernel k-Means becomes infeasible on a single computer, a fact that strongly suggests a distributed approach. In this paper, we present such an approach to the Kernel k-Means clustering algorithm, in order to make its application to a large number of samples feasible and, thus, achieve high performance clustering results on very big datasets. Our distributed approach follows the MapReduce programming model and consists of 3 stages, the kernel matrix computation, a novel matrix trimming method and the Kernel k-Means clustering algorithm.

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Section: Introductionmentioning

confidence: 95%

A distributed framework for trimmed Kernel k-Means clustering

Tsapanos

Tefas

Nikolaidis

et al. 2015

Pattern Recognition

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“…Finally, Euclidean distances in the kernel space can be measured using dot products. Kernel k-Means provides a popular starting point for many state of the art clustering schemes [18,19,8,5]. A recent survey on kernel clustering methods can be found in [6], while [9] presents a comparative study which supports the superiority of kernel clustering methods, over more conventional clustering approaches.…”

Section: Introductionmentioning

confidence: 98%

Fast Kernel Matrix Computation for Big Data Clustering

Tsapanos

Tefas

Nikolaidis

et al. 2015

Procedia Computer Science

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Kernel k-Means is a basis for many state of the art global clustering approaches. When the number of samples grows too big, however, it is extremely time-consuming to compute the entire kernel matrix and it is impossible to store it in the memory of a single computer. The algorithm of Approximate Kernel k-Means has been proposed, which works using only a small part of the kernel matrix. The computation of the kernel matrix, even a part of it, remains a significant bottleneck of the process. Some types of kernel, however, can be computed using matrix multiplication. Modern CPU architectures and computational optimization methods allow for very fast matrix multiplication, thus those types of kernel matrices can be computed much faster than others.

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“…There are many clustering techniques in the literatures. For example, [5] and [6] considered kernel-based clustering, [7] and [9] used max-margin constraint in the clustering, and [9]- [11] proposed point-based central clustering techniques, e.g., k-mean, k-median, and fuzzy c-means (FCM).…”

Section: Introductionmentioning

confidence: 99%

Fast Robust Twin Support Vector Clustering

Ye¹,

Zhao²,

Naiem³

2017

dtetr

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Abstract. This paper develops a fast k-plane clustering method called L1-norm Distance Minimization based Fast Robust TWSVC (FRTWSVC) by using robust L1-norm distance. To solve the resulted objective, we propose a novel iterative algorithm. Only a system of linear equations needs to be computed in each iteration. These characteristics make our methods more powerful and efficient than TWSVC. We also conduct some insightful analysis on the convergence of the proposed algorithms. Theoretical insights and effectiveness of our method are further supported by promising experimental results.

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Cooperative and penalized competitive learning with application to kernel-based clustering

Cited by 24 publications

References 14 publications

A distributed framework for trimmed Kernel k-Means clustering

A distributed framework for trimmed Kernel k-Means clustering

Fast Kernel Matrix Computation for Big Data Clustering

Fast Robust Twin Support Vector Clustering

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