Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically
As the central notion in semi-supervised learning, smoothness is often realized on a graph representation of the data. In this paper, we study two complementary dimensions of smoothness: its pointwise nature and probabilistic modeling. While no existing graph-based work exploits them in conjunction,...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2014
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/2249 https://ink.library.smu.edu.sg/context/sis_research/article/3249/viewcontent/fang14.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | As the central notion in semi-supervised learning, smoothness is often realized on a graph representation of the data. In this paper, we study two complementary dimensions of smoothness: its pointwise nature and probabilistic modeling. While no existing graph-based work exploits them in conjunction, we encompass both in a novel framework of Probabilistic Graph-based Pointwise Smoothness (PGP), building upon two foundational models of data closeness and label coupling. This new form of smoothness axiomatizes a set of probability constraints, which ultimately enables class prediction. Theoretically, we provide an error and robustness analysis of PGP. Empirically, we conduct extensive experiments to show the advantages of PGP. |
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