VIGAN: Missing View Imputation with Generative Adversarial Networks

Chen, Shang; Palmer, Aaron; Sun, Jianfang; Chen, Ko-Shin; Lü, Jin; Bi, Jinbo

doi:10.48550/arxiv.1708.06724

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Vanilla GAN [18] was proposed to generate desired data from random noise. Recently, some GAN [22], [23] models are designed to learn the relationship between different views. Following this line, we consider to leverage GAN model for compensating the missing data.…”

Section: Encoder Generator Networkmentioning

confidence: 99%

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Generative Partial Multi-View Clustering

Wang,

Ding,

Tao

et al. 2020

Preprint

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Nowadays, with the rapid development of data collection sources and feature extraction methods, multi-view data are getting easy to obtain and have received increasing research attention in recent years, among which, multi-view clustering (MVC) forms a mainstream research direction and is widely used in data analysis. However, existing MVC methods mainly assume that each sample appears in all the views, without considering the incomplete view case due to data corruption, sensor failure, equipment malfunction, etc. In this study, we design and build a generative partial multi-view clustering model, named as GP-MVC, to address the incomplete multi-view problem by explicitly generating the data of missing views. The main idea of GP-MVC lies at two-fold. First, multi-view encoder networks are trained to learn common low-dimensional representations, followed by a clustering layer to capture the consistent cluster structure across multiple views. Second, view-specific generative adversarial networks are developed to generate the missing data of one view conditioning on the shared representation given by other views. These two steps could be promoted mutually, where learning common representations facilitates data imputation and the generated data could further explores the view consistency. Moreover, an weighted adaptive fusion scheme is implemented to exploit the complementary information among different views. Experimental results on four benchmark datasets are provided to show the effectiveness of the proposed GP-MVC over the stateof-the-art methods.

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Section: Encoder Generator Networkmentioning

confidence: 99%

“…It shows a more powerful ability than pix2pix GAN in image translation from one domain to another, and hence effectively solves the paired sample shortage problem. GAN also gains a wide application in multiview data generation [23] and [38].…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Generative Partial Multi-View Clustering

Wang,

Ding,

Tao

et al. 2020

Preprint

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A Survey on Multi-View Clustering

Chao¹,

Sun²,

Bi³

2017

Preprint

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With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rapidly, there has not been a survey to summarize and analyze the current progress. Therefore, this paper reviews the common strategies for combining multiple views of data and based on this summary we propose a novel taxonomy of the MVC approaches. We further discuss the relationships between MVC and multi-view representation, ensemble clustering, multi-task clustering, multi-view supervised and semi-supervised learning. Several representative real-world applications are elaborated. To promote future development of MVC, we envision several open problems that may require further investigation and thorough examination.

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VIGAN: Missing View Imputation with Generative Adversarial Networks

Cited by 2 publications

References 23 publications

Generative Partial Multi-View Clustering

Generative Partial Multi-View Clustering

A Survey on Multi-View Clustering

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