Fusing separated representation into an autoencoder for magnetic materials outlier detection

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
Ying Cao, S. Ko
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引用次数: 1

Abstract

In materials science, an outlier may be due to variability in measurement, or it may indicate experimental errors. In this paper, we used an unsupervised method to remove outliers before further data-driven material analysis. Recently, autoencoder networks have achieved excellent results by minimizing reconstruction error. However, autoencoders do not promote the separation between outliers and inliers. The proposed SRAE model integrates latent representation to optimize the reconstruction error and ensures that outliers always deviate from the dataset in the compressed representation space. Experiments on the Nd-Fe-B magnetic materials dataset also show that after removing outliers with the proposed method, the prediction result of material property is significantly improved, indicating that the outlier detection effect is excellent.
将分离表示融合到用于磁性材料异常值检测的自动编码器中
在材料科学中,异常值可能是由于测量的可变性,或者它可能表明实验错误。在本文中,在进一步的数据驱动材料分析之前,我们使用了一种无监督的方法来去除异常值。近年来,自编码器网络在最小化重构误差方面取得了优异的效果。然而,自编码器并不能促进离群值和内线的分离。提出的SRAE模型集成了潜在表示,优化了重构误差,保证了在压缩的表示空间中离群点总是偏离数据集。在Nd-Fe-B磁性材料数据集上的实验也表明,采用该方法去除异常点后,材料性能的预测结果有明显改善,表明异常点检测效果良好。
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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