Partial multiview clustering with locality graph regularization

2021 ◽  
Vol 36 (6) ◽  
pp. 2991-3010
Author(s):  
Huiqiang Lian ◽  
Huiying Xu ◽  
Siwei Wang ◽  
Miaomiao Li ◽  
Xinzhong Zhu ◽  
...  
Keyword(s):  
Graph Regularization ◽  
Multiview Clustering

Semi-Supervised Dictionary Learning Based on Atom Graph Regularization

2018 ◽  
Author(s):  
Xiaoqin Zhang ◽  
Qianqian Liu ◽  
Di Wang ◽  
Jie Hu ◽  
Nannan Gu ◽  
...  
Keyword(s):  
Dictionary Learning ◽  
Graph Regularization ◽  
Supervised Dictionary Learning

Supervised discriminant Isomap with maximum margin graph regularization for dimensionality reduction

2021 ◽  
pp. 115055
Author(s):  
Hongchun Qu ◽  
Lin Li ◽  
Zhaoni Li ◽  
Jian Zheng
Keyword(s):  
Dimensionality Reduction ◽  
Graph Regularization ◽  
Maximum Margin

Scalable Semi-Supervised Learning by Efficient Anchor Graph Regularization

2016 ◽  
Vol 28 (7) ◽  
pp. 1864-1877 ◽  
Author(s):  
Meng Wang ◽  
Weijie Fu ◽  
Shijie Hao ◽  
Dacheng Tao ◽  
Xindong Wu
Keyword(s):  
Supervised Learning ◽  
Graph Regularization

scholarly journals Non-negative sparse Laplacian regularized latent multi-view subspace clustering

2021 ◽  
Vol 15 ◽  
pp. 174830262110249
Author(s):  
Cong-Zhe You ◽  
Zhen-Qiu Shu ◽  
Hong-Hui Fan
Keyword(s):  
Subspace Clustering ◽  
Structural Features ◽  
Space Representation ◽  
Graph Regularization ◽  
Structure Information ◽  
Cluster Data ◽  
Latent Space ◽  
Low Dimensional ◽  
Relationship Of ◽  
The Relationship

Recently, in the area of artificial intelligence and machine learning, subspace clustering of multi-view data is a research hotspot. The goal is to divide data samples from different sources into different groups. We proposed a new subspace clustering method for multi-view data which termed as Non-negative Sparse Laplacian regularized Latent Multi-view Subspace Clustering (NSL2MSC) in this paper. The method proposed in this paper learns the latent space representation of multi view data samples, and performs the data reconstruction on the latent space. The algorithm can cluster data in the latent representation space and use the relationship of different views. However, the traditional representation-based method does not consider the non-linear geometry inside the data, and may lose the local and similar information between the data in the learning process. By using the graph regularization method, we can not only capture the global low dimensional structural features of data, but also fully capture the nonlinear geometric structure information of data. The experimental results show that the proposed method is effective and its performance is better than most of the existing alternatives.

scholarly journals Adaptive Structure Concept Factorization for Multiview Clustering

2018 ◽  
Vol 30 (4) ◽  
pp. 1080-1103 ◽  
Author(s):  
Kun Zhan ◽  
Jinhui Shi ◽  
Jing Wang ◽  
Haibo Wang ◽  
Yuange Xie
Keyword(s):  
Nonnegative Matrix Factorization ◽  
State Of The Art ◽  
Nonnegative Matrix ◽  
Adaptive Method ◽  
Data Sets ◽  
Clustering Methods ◽  
Normalized Mutual Information ◽  
Adaptive Structure ◽  
Concept Factorization ◽  
Multiview Clustering

Most existing multiview clustering methods require that graph matrices in different views are computed beforehand and that each graph is obtained independently. However, this requirement ignores the correlation between multiple views. In this letter, we tackle the problem of multiview clustering by jointly optimizing the graph matrix to make full use of the data correlation between views. With the interview correlation, a concept factorization–based multiview clustering method is developed for data integration, and the adaptive method correlates the affinity weights of all views. This method differs from nonnegative matrix factorization–based clustering methods in that it can be applicable to data sets containing negative values. Experiments are conducted to demonstrate the effectiveness of the proposed method in comparison with state-of-the-art approaches in terms of accuracy, normalized mutual information, and purity.

Identification of Small Molecule–miRNA Associations with Graph Regularization Techniques in Heterogeneous Networks

2020 ◽  
Vol 60 (12) ◽  
pp. 6709-6721
Author(s):  
Cong Shen ◽  
Jiawei Luo ◽  
Wenjue Ouyang ◽  
Pingjian Ding ◽  
Hao Wu
Keyword(s):  
Small Molecule ◽  
Heterogeneous Networks ◽  
Graph Regularization ◽  
Regularization Techniques

Learning Multi-Graph Regularization for SVM Classification

2018 ◽  
Author(s):  
Vasileios Mygdalis ◽  
Anastasios Tefas ◽  
Ioannis Pitas
Keyword(s):  
Graph Regularization ◽  
Svm Classification

scholarly journals BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks

2021 ◽  
Vol 27 (7) ◽  
pp. 667-692
Author(s):  
Lamia Berkani ◽  
Lylia Betit ◽  
Louiza Belarif
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Recommendation Algorithms ◽  
Clustering Approach ◽  
Real World Datasets ◽  
Multiview Clustering ◽  
Improving Accuracy

Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.

Low-rank representation with adaptive graph regularization

2018 ◽  
Vol 108 ◽  
pp. 83-96 ◽  
Author(s):  
Jie Wen ◽  
Xiaozhao Fang ◽  
Yong Xu ◽  
Chunwei Tian ◽  
Lunke Fei
Keyword(s):  
Low Rank ◽  
Graph Regularization ◽  
Low Rank Representation
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