Semi-Supervised Learning with Close-Form Label Propagation Using a Bipartite Graph

In this paper, we introduce an efficient and effective algorithm for Graph-based Semi-Supervised Learning (GSSL). Unlike other GSSL methods, our proposed algorithm achieves efficiency by constructing a bipartite graph, which connects a small number of representative points to a large volume of raw d...

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Vydáno v:	Symmetry (Basel) Ročník 16; číslo 10; s. 1312
Hlavní autoři:	Peng, Zhongxing, Zheng, Gengzhong, Huang, Wei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.10.2024
Témata:	Accuracy Algorithms Analysis Approximation Closed form solutions Construction Data points Datasets Effectiveness Efficiency Exact solutions Graph theory Labels Machine learning Propagation Semi-supervised learning User generated content
ISSN:	2073-8994, 2073-8994
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Abstract	In this paper, we introduce an efficient and effective algorithm for Graph-based Semi-Supervised Learning (GSSL). Unlike other GSSL methods, our proposed algorithm achieves efficiency by constructing a bipartite graph, which connects a small number of representative points to a large volume of raw data by capturing their underlying manifold structures. This bipartite graph, with a sparse and anti-diagonal affinity matrix which is symmetrical, serves as a low-rank approximation of the original graph. Consequently, our algorithm accelerates both the graph construction and label propagation steps. In particular, on the one hand, our algorithm computes the label propagation in closed-form, reducing its computational complexity from cubic to approximately linear with respect to the number of data points; on the other hand, our algorithm calculates the soft label matrix for unlabeled data using a closed-form solution, thereby gaining additional acceleration. Comprehensive experiments performed on six real-world datasets demonstrate the efficiency and effectiveness of our algorithm in comparison to five state-of-the-art algorithms.
AbstractList	In this paper, we introduce an efficient and effective algorithm for Graph-based Semi-Supervised Learning (GSSL). Unlike other GSSL methods, our proposed algorithm achieves efficiency by constructing a bipartite graph, which connects a small number of representative points to a large volume of raw data by capturing their underlying manifold structures. This bipartite graph, with a sparse and anti-diagonal affinity matrix which is symmetrical, serves as a low-rank approximation of the original graph. Consequently, our algorithm accelerates both the graph construction and label propagation steps. In particular, on the one hand, our algorithm computes the label propagation in closed-form, reducing its computational complexity from cubic to approximately linear with respect to the number of data points; on the other hand, our algorithm calculates the soft label matrix for unlabeled data using a closed-form solution, thereby gaining additional acceleration. Comprehensive experiments performed on six real-world datasets demonstrate the efficiency and effectiveness of our algorithm in comparison to five state-of-the-art algorithms.
Audience	Academic
Author	Peng, Zhongxing Zheng, Gengzhong Huang, Wei
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SubjectTerms	Accuracy Algorithms Analysis Approximation Closed form solutions Construction Data points Datasets Effectiveness Efficiency Exact solutions Graph theory Labels Machine learning Propagation Semi-supervised learning User generated content
Title	Semi-Supervised Learning with Close-Form Label Propagation Using a Bipartite Graph
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