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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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2014/525398 |
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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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