Multiple Kernel Learning Based Multi-view Spectral Clustering

2014 ◽  
Author(s):  
Dongyan Guo ◽  
Jian Zhang ◽  
Xinwang Liu ◽  
Ying Cui ◽  
Chunxia Zhao
Keyword(s):  
Spectral Clustering ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Multiple Kernel

A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data

2020 ◽  
Author(s):  
Ren Qi ◽  
Jin Wu ◽  
Fei Guo ◽  
Lei Xu ◽  
Quan Zou
Keyword(s):  
Single Cell ◽  
Spectral Clustering ◽  
Kernel Method ◽  
Multiple Kernel Learning ◽  
Superior Performance ◽  
Kernel Learning ◽  
Support Vector ◽  
Multiple Kernel ◽  
Proposed Model ◽  
Similarity Information

Abstract Single-cell RNA-sequencing (scRNA-seq) data widely exist in bioinformatics. It is crucial to devise a distance metric for scRNA-seq data. Almost all existing clustering methods based on spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretization of the learned labels by k-means clustering. However, this common practice has potential flaws that may lead to severe information loss and degradation of performance. Furthermore, the performance of a kernel method is largely determined by the selected kernel; a self-weighted multiple kernel learning model can help choose the most suitable kernel for scRNA-seq data. To this end, we propose to automatically learn similarity information from data. We present a new clustering method in the form of a multiple kernel combination that can directly discover groupings in scRNA-seq data. The main proposition is that automatically learned similarity information from scRNA-seq data is used to transform the candidate solution into a new solution that better approximates the discrete one. The proposed model can be efficiently solved by the standard support vector machine (SVM) solvers. Experiments on benchmark scRNA-Seq data validate the superior performance of the proposed model. Spectral clustering with multiple kernels is implemented in Matlab, licensed under Massachusetts Institute of Technology (MIT) and freely available from the Github website, https://github.com/Cuteu/SMSC/.

Robust Subspace Clustering Based on Automatic Weighted Multiple Kernel Learning

2021 ◽  
Author(s):  
Guo ◽  
Xiaoqian Zhang ◽  
Zhigui Liu ◽  
Xuqian Xue ◽  
Qian Wang ◽  
...  
Keyword(s):  
Multiple Kernel Learning ◽  
Subspace Clustering ◽  
Kernel Learning ◽  
Multiple Kernel

scholarly journals Pseudo-Marginal Bayesian Multiple-Class Multiple-Kernel Learning for Neuroimaging Data

2014 ◽  
Author(s):  
Andrew D. O'Harney ◽  
Andre Marquand ◽  
Katya Rubia ◽  
Kaylita Chantiluke ◽  
Anna Smith ◽  
...  
Keyword(s):  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Multiple Kernel ◽  
Neuroimaging Data ◽  
Multiple Class
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