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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Vydáno v:	IET image processing Ročník 13; číslo 14; s. 2753 - 2762
Hlavní autoři:	Chen, Long, Zhong, Zhi
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 12.12.2019
Témata:	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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