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

Content Provider	IET Digital Library
Author	Chen, Long Zhong, Zhi
Abstract	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.
Starting Page	2753
Ending Page	2762
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 14, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6363
Journal	IET Image Processing
Publisher Date	2019-07-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Combinatorial Mathematics Data Handling Technique 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 Interpolation And Function Approximation Iterative Method Iterative Optimisation Strategy Knowledge Engineering Technique Label Domain Label Information Learning in AI Linear Algebra Matrix Algebra Noise Point Numerical Analysis Optimisation Optimisation Technique Pattern Classification PGSTL Progressive Graph-based SubSpace Transductive Learning Representative Relation Matrix Semisupervised Classification Sufficient Labelled Sample
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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