Is the whole greater than the sum of its parts?
The PART-WHOLE relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either sep...
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sg-smu-ink.sis_research-63592020-11-19T07:21:24Z Is the whole greater than the sum of its parts? LI, Liangyue TONG, Hanghang WANG, Yong SHI, Conglei CAO, Nan BUCHLER, Norbou The PART-WHOLE relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either separate models or linear joint models, which assume the outcome of the parts has a linear and independent effect on the outcome of the whole. In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes. The proposed method offers two distinct advantages over the existing work. First (Model Generality), we formulate joint PART-WHOLE outcome prediction as a generic optimization problem, which is able to encode a variety of complex relationships between the outcome of the whole and parts, beyond the linear independence assumption. Second (Algorithm Efficacy), we propose an effective and efficient block coordinate descent algorithm, which is able to find the coordinate-wise optimum with a linear complexity in both time and space. Extensive empirical evaluations on real-world datasets demonstrate that the proposed PAROLE (1) leads to consistent prediction performance improvement by modeling the non-linear part-whole relationship as well as part-part interdependency, and (2) scales linearly in terms of the size of the training dataset. © 2017 ACM. 2017-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5355 info:doi/10.1145/3097983.3098006 https://ink.library.smu.edu.sg/context/sis_research/article/6359/viewcontent/3097983.3098006.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Joint predictive model part-whole relationship Databases and Information Systems Data Storage Systems Software Engineering |
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Joint predictive model part-whole relationship Databases and Information Systems Data Storage Systems Software Engineering LI, Liangyue TONG, Hanghang WANG, Yong SHI, Conglei CAO, Nan BUCHLER, Norbou Is the whole greater than the sum of its parts? |
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The PART-WHOLE relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either separate models or linear joint models, which assume the outcome of the parts has a linear and independent effect on the outcome of the whole. In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes. The proposed method offers two distinct advantages over the existing work. First (Model Generality), we formulate joint PART-WHOLE outcome prediction as a generic optimization problem, which is able to encode a variety of complex relationships between the outcome of the whole and parts, beyond the linear independence assumption. Second (Algorithm Efficacy), we propose an effective and efficient block coordinate descent algorithm, which is able to find the coordinate-wise optimum with a linear complexity in both time and space. Extensive empirical evaluations on real-world datasets demonstrate that the proposed PAROLE (1) leads to consistent prediction performance improvement by modeling the non-linear part-whole relationship as well as part-part interdependency, and (2) scales linearly in terms of the size of the training dataset. © 2017 ACM. |
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LI, Liangyue TONG, Hanghang WANG, Yong SHI, Conglei CAO, Nan BUCHLER, Norbou |
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LI, Liangyue TONG, Hanghang WANG, Yong SHI, Conglei CAO, Nan BUCHLER, Norbou |
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LI, Liangyue |
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Is the whole greater than the sum of its parts? |
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Is the whole greater than the sum of its parts? |
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Is the whole greater than the sum of its parts? |
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Is the whole greater than the sum of its parts? |
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Is the whole greater than the sum of its parts? |
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is the whole greater than the sum of its parts? |
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Institutional Knowledge at Singapore Management University |
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2017 |
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https://ink.library.smu.edu.sg/sis_research/5355 https://ink.library.smu.edu.sg/context/sis_research/article/6359/viewcontent/3097983.3098006.pdf |
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