Multiview Clustering by Joint Latent Representation and Similarity Learning

Xie, Deyan; Zhang, Xiangdong; Gao, Quanxue; Han, Jiale; Xiao, Song; Gao, Xinbo

doi:10.1109/tcyb.2019.2922042

Cited by 68 publications

(24 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To handle the problem of multi-view graph node embedding, some researchers have made some attempts. In the medical field, [Zhang et al, 2018b] propose a method based on GCNs for fusing multiple modalities of brain images in relationship prediction, which is useful for distinguishing Parkinson's Disease cases (a prevalent neurodegenerative disease) from controls. Another novel model [Geng et al, 2019], called spatiotemporal multi-graph convolution network, encodes the non-Euclidean correlations among regions using multiple graphs and explicitly captures them using multigraph convolution encoder.…”

Section: Related Workmentioning

confidence: 99%

“…Multi-view clustering is a fundamental task in machine learning. It aims to integrate multiple features and discover consistent information among different views [Xie et al, 2019;Zhang et al, 2018a]. Existing multi-view clustering methods have achieved considerable results in the Euclidean domains [Andrew et al, 2013;Gao et al, 2020].…”

Section: Introductionmentioning

confidence: 99%

“…To tackle this challenge, some research attempts are made in using GNNs for multi-view data of multiple graph structures. For example, multi-view graphs are used for prediction and classification of drug similarity and medicine in the medical filed [Zhang et al, 2018b;Ma et al, 2018], and are also leveraged for ridehailing demand forecasting and global poverty [Geng et al, 2019;Khan and Blumenstock, 2019]. However, those multi-view graph neural networks have the following limitations: 1) They cannot allocate learnable specifying different weights to different nodes in neighborhood; 2) They may neglect to proceed the reconstruction of both node attributes and graph structure to improve the robustness;…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multi-View Attribute Graph Convolution Networks for Clustering

Cheng

Wang

Tao

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

Self Cite

103

View full text Add to dashboard Cite

Graph neural networks (GNNs) have made considerable achievements in processing graph-structured data. However, existing methods can not allocate learnable weights to different nodes in the neighborhood and lack of robustness on account of neglecting both node attributes and graph reconstruction. Moreover, most of multi-view GNNs mainly focus on the case of multiple graphs, while designing GNNs for solving graph-structured data of multi-view attributes is still under-explored. In this paper, we propose a novel Multi-View Attribute Graph Convolution Networks (MAGCN) model for the clustering task. MAGCN is designed with two-pathway encoders that map graph embedding features and learn the view-consistency information. Specifically, the first pathway develops multi-view attribute graph attention networks to reduce the noise/redundancy and learn the graph embedding features for each multi-view graph data. The second pathway develops consistent embedding encoders to capture the geometric relationship and probability distribution consistency among different views, which adaptively finds a consistent clustering embedding space for multi-view attributes. Experiments on three benchmark graph datasets show the superiority of our method compared with several state-of-the-art algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-View Attribute Graph Convolution Networks for Clustering

Cheng

Wang

Tao

et al. 2020

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence

Self Cite

103

View full text Add to dashboard Cite

show abstract

“…The basic idea is to embed a network into a low-dimensional vector space where the inherent structural properties of the network are preserved so that the network analysis and prediction tasks can be conducted in the vector space (Zhu et al 2018). Recent work on network embedding methods have been demonstrated to be effective in a variety of applications such as link prediction (Yu et al 2017), classification (Sun, Yuan, and, and clustering (Xie et al 2019;Peng et al 2019). Generally, most of the existing methods for network embedding focus on static networks (Zhang, Lyu, and Zhang 2018;Wang et al 2017;Perozzi, Al-Rfou, and Skiena 2014).…”

Section: Introductionmentioning

confidence: 99%

Temporal Network Embedding with High-Order Nonlinear Information

Qiu

et al. 2020

AAAI

View full text Add to dashboard Cite

Temporal network embedding, which aims to learn the low-dimensional representations of nodes in temporal networks that can capture and preserve the network structure and evolution pattern, has attracted much attention from the scientific community. However, existing methods suffer from two main disadvantages: 1) they cannot preserve the node temporal proximity that capture important properties of the network structure; and 2) they cannot represent the nonlinear structure of temporal networks. In this paper, we propose a high-order nonlinear information preserving (HNIP) embedding method to address these issues. Specifically, we define three orders of temporal proximities by exploring network historical information with a time exponential decay model to quantify the temporal proximity between nodes. Then, we propose a novel deep guided auto-encoder to capture the highly nonlinear structure. Meanwhile, the training set of the guide auto-encoder is generated by the temporal random walk (TRW) algorithm. By training the proposed deep guided auto-encoder with a specific mini-batch stochastic gradient descent algorithm, HNIP can efficiently preserves the temporal proximities and highly nonlinear structure of temporal networks. Experimental results on four real-world networks demonstrate the effectiveness of the proposed method.

show abstract

“…C Lustering, primitive exploration with little or no prior knowledge, is one of the most indispensable and fundamental research topics in artificial intelligence research, and applies in many fields such as image retrieval, image annotation, document analysis and image segmentation, etc. In the past few decades, many classic clustering algorithms have been proposed, including spectral clustering (SC) [1], [2], subspace clustering [3], [4], graph based clustering [5] and so on. Despite extensive study, the performance of traditional clustering methods deteriorates with high dimensional data due to unreliable similarity metrics, known as the curse of dimensionality, when working with large-scale real-world image datasets.…”

Section: Introductionmentioning

confidence: 99%

Regression Based Clustering by Deep Adversarial Learning

Tang

Zhang

2020

IEEE Access

View full text Add to dashboard Cite

Despite the great success, existing regression clustering methods based on shallow models are vulnerable due to: (1) They often pay no attention to the combination between learning representations and clustering, thus resulting in unsatisfactory clustering performance. (2) They ignore the relationship of data distribution and target distribution such that those methods are noise and illumination-change sensitive. (3) These nonlinear regression methods usually impose the hard constraint to minimize the mismatch between the discrete cluster assignment matrix and latent representations, which leads to over-fitting. In this paper, we utilize deep adversarial regression to tackle these problems and formulate regression based clustering by deep adversarial learning (RCDA). By seamlessly combining with the stacked autoencoder, the proposed model integrates learning deep nonlinear latent representation and clustering in a unified framework. Specifically, RCDA uses a kind of relax constraint between latent representations and continuous cluster assignment matrix to avoid over-fitting, and simultaneously utilizes the t-SNE algorithm and adversarial learning to analyze data distribution and target distribution so that improve representations learning. Experimental results on public benchmark datasets demonstrate that the proposed architecture achieves better performance than state-of-the-art clustering models in image clustering task. INDEX TERMS Unsupervised learning, image clustering, regression based clustering.

show abstract

Multiview Clustering by Joint Latent Representation and Similarity Learning

Cited by 68 publications

References 24 publications

Multi-View Attribute Graph Convolution Networks for Clustering

Multi-View Attribute Graph Convolution Networks for Clustering

Temporal Network Embedding with High-Order Nonlinear Information

Regression Based Clustering by Deep Adversarial Learning

Contact Info

Product

Resources

About