{"title":"Fusing separated representation into an autoencoder for magnetic materials outlier detection","authors":"Ying Cao, S. Ko","doi":"10.1080/21642583.2022.2052995","DOIUrl":null,"url":null,"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.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2022.2052995","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
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.
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