A semi-supervised learning algorithm via adaptive Laplacian graph

Many semi-supervised learning methods have been developed in recent years, especially graph-based approaches, which have achieved satisfactory performance in the practical applications. There are two points that need to be noticed. Firstly, the quality of the graph directly affects the final classif...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) Vol. 426; pp. 162 - 173
Main Authors: Yuan, Yuan, Li, Xin, Wang, Qi, Nie, Feiping
Format: Journal Article
Language:English
Published: Elsevier B.V 22.02.2021
Subjects:
Adaptive graph
Pattern recognition
Semi-supervised learning
Semi-supervised learning
Adaptive graph
Pattern recognition
ISSN:0925-2312, 1872-8286
Online Access:Get full text
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Summary:Many semi-supervised learning methods have been developed in recent years, especially graph-based approaches, which have achieved satisfactory performance in the practical applications. There are two points that need to be noticed. Firstly, the quality of the graph directly affects the final classification accuracy. However, graph-based algorithms mostly use k-Nearest Neighbor to construct the graph. And the directly constructed graph is inaccurate due to outliers and erroneous features in the data. Secondly, the amount of labeled data is a small part of all data. It cannot be guaranteed that all categories of data are included in the labeled data and the labels of data are not totally correct in practice. To address the aforementioned problems, we propose a new graph-based semi-supervised method named ALGSSL via adaptive Laplacian graph. In the algorithm, we adaptively update the graph to reduce the sensitiveness of the construction of initial graph. Meanwhile, we use the regularization parameters to set confidence on existing labels, which can reduce the impact of the error labels on the result and discover the new category. Experiments on three toy datasets and nine benchmark datasets demonstrate the proposed method can achieve good performance.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.09.069