A Novel Hierarchical Deep Matrix Completion Method

The matrix completion technique based on matrix factorization for recovering missing items is widely used in collaborative filtering, image restoration, and other applications. We proposed a new matrix completion model called hierarchical deep matrix completion (HDMC), where we assume that the varia...

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Autores principales:	Yaru Chen, Xiaohong Gu, Conghua Zhou, Xiaolong Zhu, Yi Jiang, John Kingsley Arthur, Eric Appiah Mantey, Ernest Domanaanmwi Ganaa
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Matrix completion hierarchical relation structured sparsity regulation neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3f365e77f52f4a93b4a52aa3a2aac4e9
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id	oai:doaj.org-article:3f365e77f52f4a93b4a52aa3a2aac4e9
record_format	dspace
spelling	oai:doaj.org-article:3f365e77f52f4a93b4a52aa3a2aac4e92021-11-19T00:04:37ZA Novel Hierarchical Deep Matrix Completion Method2169-353610.1109/ACCESS.2021.3049297https://doaj.org/article/3f365e77f52f4a93b4a52aa3a2aac4e92021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9313993/https://doaj.org/toc/2169-3536The matrix completion technique based on matrix factorization for recovering missing items is widely used in collaborative filtering, image restoration, and other applications. We proposed a new matrix completion model called hierarchical deep matrix completion (HDMC), where we assume that the variables lie in hierarchically organized groups. HDMC explicitly expresses either shallow or high-level hierarchical structures, such as taxonomy trees, by embedding a series of so-called structured sparsity penalties in a framework to encourage hierarchical relations between compact representations and reconstructed data. Moreover, HDMC considers the group-level sparsity of neurons in a neural network to obtain a pruning effect and compact architecture by enhancing the relevance of within-group neurons while neglecting the between-group neurons. Since the optimization of HDMC is a nonconvex problem, to avoid converting the framework of the HDMC models into separate optimized formulations, we unify a generic optimization by applying a smoothing proximal gradient strategy in dual space. HDMC is compared with state-of-the-art matrix completion methods on applications with simulated data, MRI image datasets, and gene expression datasets. The experimental results verify that HDMC achieves higher matrix completion accuracy.Yaru ChenXiaohong GuConghua ZhouXiaolong ZhuYi JiangJohn Kingsley ArthurEric Appiah ManteyErnest Domanaanmwi GanaaIEEEarticleMatrix completionhierarchical relationstructured sparsityregulationneural networkElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 7908-7920 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Matrix completion hierarchical relation structured sparsity regulation neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Matrix completion hierarchical relation structured sparsity regulation neural network Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yaru Chen Xiaohong Gu Conghua Zhou Xiaolong Zhu Yi Jiang John Kingsley Arthur Eric Appiah Mantey Ernest Domanaanmwi Ganaa A Novel Hierarchical Deep Matrix Completion Method
description	The matrix completion technique based on matrix factorization for recovering missing items is widely used in collaborative filtering, image restoration, and other applications. We proposed a new matrix completion model called hierarchical deep matrix completion (HDMC), where we assume that the variables lie in hierarchically organized groups. HDMC explicitly expresses either shallow or high-level hierarchical structures, such as taxonomy trees, by embedding a series of so-called structured sparsity penalties in a framework to encourage hierarchical relations between compact representations and reconstructed data. Moreover, HDMC considers the group-level sparsity of neurons in a neural network to obtain a pruning effect and compact architecture by enhancing the relevance of within-group neurons while neglecting the between-group neurons. Since the optimization of HDMC is a nonconvex problem, to avoid converting the framework of the HDMC models into separate optimized formulations, we unify a generic optimization by applying a smoothing proximal gradient strategy in dual space. HDMC is compared with state-of-the-art matrix completion methods on applications with simulated data, MRI image datasets, and gene expression datasets. The experimental results verify that HDMC achieves higher matrix completion accuracy.
format	article
author	Yaru Chen Xiaohong Gu Conghua Zhou Xiaolong Zhu Yi Jiang John Kingsley Arthur Eric Appiah Mantey Ernest Domanaanmwi Ganaa
author_facet	Yaru Chen Xiaohong Gu Conghua Zhou Xiaolong Zhu Yi Jiang John Kingsley Arthur Eric Appiah Mantey Ernest Domanaanmwi Ganaa
author_sort	Yaru Chen
title	A Novel Hierarchical Deep Matrix Completion Method
title_short	A Novel Hierarchical Deep Matrix Completion Method
title_full	A Novel Hierarchical Deep Matrix Completion Method
title_fullStr	A Novel Hierarchical Deep Matrix Completion Method
title_full_unstemmed	A Novel Hierarchical Deep Matrix Completion Method
title_sort	novel hierarchical deep matrix completion method
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/3f365e77f52f4a93b4a52aa3a2aac4e9
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A Novel Hierarchical Deep Matrix Completion Method

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