Matrix Factorization for Evolution Data

We study a matrix factorization problem, that is, to find two factor matrices U and V such that R≈UT×V, where R is a matrix composed of the values of the objects O1,O2,…,On at consecutive time points T1,T2,…,Tt. We first present MAFED, a constrained optimization model for this problem, which straigh...

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Main Authors: Xiao-Yu Huang, Xian-Hong Xiang, Wubin Li, Kang Chen, Wen-Xue Cai, Lei Li
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2014/525398
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spelling doaj-25e0c8a7f7b64650815b1791e513c57f2020-11-24T21:39:47ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472014-01-01201410.1155/2014/525398525398Matrix Factorization for Evolution DataXiao-Yu Huang0Xian-Hong Xiang1Wubin Li2Kang Chen3Wen-Xue Cai4Lei Li5Software Institute, Sun Yat-Sen University, Guangzhou 510275, ChinaDepartment of Interventional Radiology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, ChinaDepartment of Computing Science, Umeå University, 901 87 Umeå, SwedenAcademy of Guangdong Telecom Co. Ltd., Guangzhou 510630, ChinaSchool of Economics and Commerce, South China University of Technology, Guangzhou 510006, ChinaSoftware Institute, Sun Yat-Sen University, Guangzhou 510275, ChinaWe study a matrix factorization problem, that is, to find two factor matrices U and V such that R≈UT×V, where R is a matrix composed of the values of the objects O1,O2,…,On at consecutive time points T1,T2,…,Tt. We first present MAFED, a constrained optimization model for this problem, which straightforwardly performs factorization on R. Then based on the interplay of the data in U, V, and R, a probabilistic graphical model using the same optimization objects is constructed, in which structural dependencies of the data in these matrices are revealed. Finally, we present a fitting algorithm to solve the proposed MAFED model, which produces the desired factorization. Empirical studies on real-world datasets demonstrate that our approach outperforms the state-of-the-art comparison algorithms.http://dx.doi.org/10.1155/2014/525398
collection DOAJ
language English
format Article
sources DOAJ
author Xiao-Yu Huang
Xian-Hong Xiang
Wubin Li
Kang Chen
Wen-Xue Cai
Lei Li
spellingShingle Xiao-Yu Huang
Xian-Hong Xiang
Wubin Li
Kang Chen
Wen-Xue Cai
Lei Li
Matrix Factorization for Evolution Data
Mathematical Problems in Engineering
author_facet Xiao-Yu Huang
Xian-Hong Xiang
Wubin Li
Kang Chen
Wen-Xue Cai
Lei Li
author_sort Xiao-Yu Huang
title Matrix Factorization for Evolution Data
title_short Matrix Factorization for Evolution Data
title_full Matrix Factorization for Evolution Data
title_fullStr Matrix Factorization for Evolution Data
title_full_unstemmed Matrix Factorization for Evolution Data
title_sort matrix factorization for evolution data
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2014-01-01
description We study a matrix factorization problem, that is, to find two factor matrices U and V such that R≈UT×V, where R is a matrix composed of the values of the objects O1,O2,…,On at consecutive time points T1,T2,…,Tt. We first present MAFED, a constrained optimization model for this problem, which straightforwardly performs factorization on R. Then based on the interplay of the data in U, V, and R, a probabilistic graphical model using the same optimization objects is constructed, in which structural dependencies of the data in these matrices are revealed. Finally, we present a fitting algorithm to solve the proposed MAFED model, which produces the desired factorization. Empirical studies on real-world datasets demonstrate that our approach outperforms the state-of-the-art comparison algorithms.
url http://dx.doi.org/10.1155/2014/525398
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