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In this paper, we propose a novel sparse multiple kernel k$$ k $$‐means (SMKKM) clustering by introducing a 1$$ {\\ell}_1 $$‐norm to induce the sparsity of the partition matrix. We then design an efficient alternating algorithm with curve search technology. More import...
对于单核方法,作者比较了核k-means(KKM),谱聚类(SC),鲁棒核k-means(RKKM)以及提出的SCSK算法; 对于多核算法(本论文中设定了12个核),作者比较了MKKM(Multiple Kernel K-means)算法、AASC(Affinity aggregation for SC)算法、RMKKM(Robust Multiple Kernel K-means Using L21-norm)算法以及提出的SCMK算法。
As a representative of multiple kernel clustering (MKC), simple multiple kernel k-means (SimpleMKKM) is recently put forward to boosting the clustering performance by optimally fusing a group of pre-specified kernel matrices. Despite achieving significant improvement in a variety of applications, we...
arXiv:2005.04975v2 [cs.LG] 12 May 2020SimpleMKKM: Simple Multiple Kernel K-meansXinwang Liu 1 En Zhu 1 Jiyuan Liu1Timothy Hospedales 2 Yang Wang 3 Meng Wang 3AbstractWe propose a simple yet effective multiple ker-nel clustering algorithm, termed simple multi-ple kernel k-means (SimpleMK...
2.1. Multiple Kernel K-means Given X ∈ Rn×d with n and d the number of samples and feature dimensions, k-means clustering aims to group X into k clusters. Let Z ∈ {0, 1}n×k be a clustering as- signment matrix, where Ziq = 1 if xi belongs to the q-th cluster, other Ziq...
This paper presents a common algorithm for the kernel k-harmonic means (KKHM) and the kernel fuzzy c-means (KFCM) clustering problems. We incorporate kernel functions in a generalized fuzzy c- means cost function, forming the cost function of a kernelized general fuzzy c-means (KGFCM) proble...
kkmeans_train.m lmkkmeans_train.R lmkkmeans_train.m mkkmeans_train.R mkkmeans_train.m Repository files navigation README This repository contains Matlab and R implementations of the clustering algorithms in "Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology...
Most kernel-based methods, such as kernel regression, kernel PCA, ICA, or k-means clustering, do not scale to large datasets, because constructing and storing the kernel matrix Kn requires at least O(n2) time and space for n samples. Rec... D Calandriello,A Lazaric,M Valko 被引量: ...
(Saunders, Gammerman, & Vovk, 1998), Kernel K-means (KK-means) (Camastra, & Verri, 2005), Spectral Clustering (SC) (Szymkowiak-Have, Girolami & Larsen, 2006), Canonical Correlation Analysis (CCA) (Lai & Fyfe, 2000), Novelty Detection (ND) (Schölkopf, Williamson, Smola, & Shawe-...