Progressive graph-based subspace transductive learning for semi-supervised classification

Graph-based transductive learning (GTL) is the efficient semi-supervised learning technique which is always employed in that sufficient labeled samples can not be obtained. Conventional GTL methods generally construct a inaccurate graph in feature domain and they are not able to align feature inform...

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Veröffentlicht in:	IET image processing Jg. 13; H. 14; S. 2753 - 2762
Hauptverfasser:	Chen, Long, Zhong, Zhi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 12.12.2019
Schlagworte:	efficient semisupervised learning technique feature affinity matrix feature domain feature information feature relationships feature‐to‐label alignment fixed subject‐wise graph graph theory high‐dimensional feature space iterative methods iterative optimisation strategy label domain label information learning (artificial intelligence) matrix algebra noise points optimisation pattern classification PGSTL progressive graph‐based subspace transductive learning representative relation matrix semisupervised classification Special Section: Adversarial Learning in Image Processing sufficient labelled samples label domain pattern classification iterative methods sufficient labelled samples representative relation matrix graph theory high-dimensional feature space feature information semisupervised classification feature affinity matrix matrix algebra noise points progressive graph-based subspace transductive learning feature relationships optimisation feature domain efficient semisupervised learning technique fixed subject-wise graph feature-to-label alignment learning (artificial intelligence) PGSTL iterative optimisation strategy label information
ISSN:	1751-9659, 1751-9667
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Beschreibung
Zusammenfassung:	Graph-based transductive learning (GTL) is the efficient semi-supervised learning technique which is always employed in that sufficient labeled samples can not be obtained. Conventional GTL methods generally construct a inaccurate graph in feature domain and they are not able to align feature information with label information. To address these issues, we propose an approach called Progressive Graph-based subspace transductive learning (PGSTL) in this paper. PGSTL gradually find the intrinsic relationship between samples that more accurately aligns feature with label. Meanwhile, PGSTL develops a feature affinity matrix in the subspace of original high-dimensional feature space, which effectively reduce the interference of noise points. And then, the representative relation matrix and the feature affinity matrix are optimized by iterative optimization strategy and finally aligned. In this way, PGSTL can not only effectively reduce the interference of noisy points, but also comprehensively consider the information in the feature and label domain of data. Extensive experimental results on various benchmark datasets demonstrate that the PGSTL achieves the best performance compared to some state-of-the-art semi-supervised learning methods.
ISSN:	1751-9659 1751-9667
DOI:	10.1049/iet-ipr.2018.6363