Multi-view Spectral Clustering with Adaptive Graph Learning and Tensor Schatten p-norm

2021 ◽  
Author(s):  
Yujiao Zhao ◽  
Yu Yun ◽  
Xiangdong Zhang ◽  
Qin Li ◽  
Quanxue Gao
Keyword(s):  
Spectral Clustering ◽  
Graph Learning

Multi-view spectral clustering via sparse graph learning

2020 ◽  
Vol 384 ◽  
pp. 1-10 ◽  
Author(s):  
Zhanxuan Hu ◽  
Feiping Nie ◽  
Wei Chang ◽  
Shuzheng Hao ◽  
Rong Wang ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Sparse Graph ◽  
Graph Learning

Incremental multi-view spectral clustering with sparse and connected graph learning

2021 ◽  
Vol 144 ◽  
pp. 260-270
Author(s):  
Hongwei Yin ◽  
Wenjun Hu ◽  
Zhao Zhang ◽  
Jungang Lou ◽  
Minmin Miao
Keyword(s):  
Connected Graph ◽  
Spectral Clustering ◽  
Graph Learning

Incomplete Multiview Spectral Clustering With Adaptive Graph Learning

2020 ◽  
Vol 50 (4) ◽  
pp. 1418-1429 ◽  
Author(s):  
Jie Wen ◽  
Yong Xu ◽  
Hong Liu
Keyword(s):  
Spectral Clustering ◽  
Graph Learning

Multi-view spectral clustering by simultaneous consensus graph learning and discretization

2021 ◽  
pp. 107632
Author(s):  
Guo Zhong ◽  
Ting Shu ◽  
Guoheng Huang ◽  
Xueming Yan
Keyword(s):  
Spectral Clustering ◽  
Graph Learning

scholarly journals Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Jian Liu ◽  
Shuguang Ge ◽  
Yuhu Cheng ◽  
Xuesong Wang
Keyword(s):  
Spectral Clustering ◽  
Cluster Structure ◽  
Biological Data ◽  
Data Matrix ◽  
Omics Data ◽  
Similarity Matrix ◽  
Graph Learning ◽  
Smooth Representation ◽  
Cancer Subtypes ◽  
Cancer Data

It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data. In recent years, some multi-view clustering algorithms have been proposed and applied to the prediction of cancer subtypes. Among them, the multi-view clustering methods based on graph learning are widely concerned. These multi-view approaches usually have one or more of the following problems. Many multi-view algorithms use the original omics data matrix to construct the similarity matrix and ignore the learning of the similarity matrix. They separate the data clustering process from the graph learning process, resulting in a highly dependent clustering performance on the predefined graph. In the process of graph fusion, these methods simply take the average value of the affinity graph of multiple views to represent the result of the fusion graph, and the rich heterogeneous information is not fully utilized. To solve the above problems, in this paper, a Multi-view Spectral Clustering Based on Multi-smooth Representation Fusion (MRF-MSC) method was proposed. Firstly, MRF-MSC constructs a smooth representation for each data type, which can be viewed as a sample (patient) similarity matrix. The smooth representation can explicitly enhance the grouping effect. Secondly, MRF-MSC integrates the smooth representation of multiple omics data to form a similarity matrix containing all biological data information through graph fusion. In addition, MRF-MSC adaptively gives weight factors to the smooth regularization representation of each omics data by using the self-weighting method. Finally, MRF-MSC imposes constrained Laplacian rank on the fusion similarity matrix to get a better cluster structure. The above problems can be transformed into spectral clustering for solving, and the clustering results can be obtained. MRF-MSC unifies the above process of graph construction, graph fusion and spectral clustering under one framework, which can learn better data representation and high-quality graphs, so as to achieve better clustering effect. In the experiment, MRF-MSC obtained good experimental results on the TCGA cancer data sets.

Multi-view feature selection via Nonnegative Structured Graph Learning

2020 ◽  
Vol 387 ◽  
pp. 110-122 ◽  
Author(s):  
Xiangpin Bai ◽  
Lei Zhu ◽  
Cheng Liang ◽  
Jingjing Li ◽  
Xiushan Nie ◽  
...  
Keyword(s):  
Feature Selection ◽  
Graph Learning

A Method for Multi-HVDC Grouping Based on Spectral Clustering

2020 ◽  
Author(s):  
Xiaohui Wang ◽  
Yu Bai ◽  
Yadong Gao ◽  
Dong Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Spectral Clustering

scholarly journals A Novel Unsupervised Classification Method for Sandy Land Using Fully Polarimetric SAR Data

2021 ◽  
Vol 13 (3) ◽  
pp. 355
Author(s):  
Weixian Tan ◽  
Borong Sun ◽  
Chenyu Xiao ◽  
Pingping Huang ◽  
Wei Xu ◽  
...  
Keyword(s):  
Spectral Clustering ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Unsupervised Classification ◽  
Classification Method ◽  
Sandy Land ◽  
Classification Methods ◽  
The Many ◽  
Representative Points

Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.

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