SparseGraphX: exponentially regularized optimal sparse graph for enhanced label propagation

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kanimozhi M, Sudhakar MS
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Abstract

Graph-based semi-supervised learning’s inherent ability to exploit the underlying structure of data distribution for supplementing label propagation has gained momentum over recent years. However, its effectiveness highly relies on the graph's structure quality and deteriorates when dealing with high dimensional, noisy, unevenly distributed data thus, necessitating adaptivity with sparsity in graph construction. To achieve this, an Exponentially Regularized Optimal Sparse Graph (EROSG) is introduced that inculcates these characteristics by exploring local connectivity ensuring efficient label propagation with reduced complexity. Accordingly, EROSG constructs the affinity matrix using a novel distance metric to widen the sample-wise interclass deviation and strengthen the local connectivity. The resulting affinity matrix is then optimized by Lagrangian multipliers with non-negative and SoftMax constraints to yield the adaptive sparse graph facilitating label propagation. Extensive analysis of EROSG on diverse datasets demonstrates consistent and superior accuracy of over 93% with a minimum availability of 5–10% of labeled data which is lacking in its competitors. Also, EROSG’s parameter-free nature lessens realization complexity emphasizing the need of the hour.

Graphical abstract

Abstract Image

SparseGraphX:用于增强标签传播的指数正则化最优稀疏图
近年来,基于图的半监督学习利用数据分布的底层结构来补充标签传播的固有能力获得了发展势头。然而,它的有效性高度依赖于图的结构质量,在处理高维、有噪声、分布不均匀的数据时,它的有效性会下降,因此需要在图的构造中使用稀疏度自适应。为了实现这一点,引入了指数正则化最优稀疏图(EROSG),通过探索局部连通性来注入这些特征,确保有效的标签传播,同时降低了复杂性。因此,EROSG使用一种新的距离度量来构建亲和矩阵,以扩大样本类间偏差并增强局部连通性。然后通过非负约束和SoftMax约束的拉格朗日乘法器对生成的亲和矩阵进行优化,生成有利于标签传播的自适应稀疏图。对EROSG在不同数据集上的广泛分析表明,EROSG具有超过93%的一致性和卓越的准确性,其标记数据的最低可用性为5-10%,这是其竞争对手所缺乏的。此外,EROSG的无参数特性降低了实现的复杂性,强调了对时间的需求。图形抽象
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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