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Interactive labelling of a multivariate dataset for supervised machine learning using linked visualisations,clustering,and active learning

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Visual Informatics 2019年第1期3卷 9-17页

作者： Mohammad Chegini Jürgen Bernard Philip Berger alexei sourin Keith Andrews Tobias Schreck Graz University of Technology Austria School of Computer Science and Engineering Nanyang Technological UniversitySingapore TU Darmstadt Germany University of Rostock Germany

Supervised machine learning techniques require labelled multivariate training *** approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive *** appropriate techniques,analysts can play an active role in a highly interactive and iterative machine learning process to label the dataset and create meaningful *** this principle has been implemented either for unsupervised,semi-supervised,or supervised machine learning tasks,the combination of all three methodologies remains *** this paper,a visual analytics approach is presented,combining a variety of machine learning capabilities with four linked visualisation views,all integrated within the mVis(multivariate Visualiser)*** available palette of techniques allows an analyst to perform exploratory data analysis on a multivariate dataset and divide it into meaningful labelled partitions,from which a classifier can be *** the workflow,the analyst can label interesting patterns or outliers in a semi-supervised process supported by active *** a dataset has been interactively labelled,the analyst can continue the workflow with supervised machine learning to assess to what degree the subsequent classifier has effectively learned the concepts expressed in the labelled training *** a novel technique called automatic dimension selection,interactions the analyst had with dimensions of the multivariate dataset are used to steer the machine learning algorithms.A real-world football dataset is used to show the utility of mVis for a series of analysis and labelling tasks,from initial labelling through iterations of data exploration,clustering,classification,and active learning to refine the named partitions,to finally producing a high-quality labelled training dataset suitable for training a *** tool empowers the analyst with interactive visualisations including scatterplots,parallel

关键词： Labelling Clustering Classification Active learning Multivariate data Visualisation

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Interactive visual labelling versus active learning:an experimental comparison

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Frontiers of Information Technology & Electronic Engineering 2020年第4期21卷 524-535页

作者： Mohammad CHEGINI Jurgen BERNARD Jian CUI Fatemeh CHEGINI alexei sourin Keith ANDREWS Tobias SCHRECK Institute of Computer Graphics and Knowledge Visualisation Graz University of TechnologyGraz 8010Austria School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore InfoVis Group University of British ColumbiaVancouver V6T1Z4Canada Max Planck Institute for Meteorology Hamburg 20146Germany Institute of Interactive Systems and Data Science Graz University of TechnologyGraz 8010Austria

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data *** quality of supervised maching learning depends not only on the type of algorithm used,but also on the quality of the labelled dataset used to train the *** instances in a training dataset is often done manually relying on selections and annotations by expert analysts,and is often a tedious and time-consuming *** learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning *** visual labelling techniques are a promising alternative,providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a *** putting the analyst in the loop,higher accuracy can be achieved in the resulting *** initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning,many aspects of these techniques are still largely *** paper presents a study conducted using the mVis tool to compare three interactive visualisations,similarity map,scatterplot matrix(SPLOM),and parallel coordinates,with each other and with active learning for the purpose of labelling a multivariate *** results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy,and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for *** also employ different labelling strategies depending on the visualisation used.

关键词： Interactive visual labelling Active learning Visual analytics

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网格模型PDE曲面重建中的边界曲线构造

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浙江大学学报（理学版） 2014年第6期41卷 611-618,623页

作者：庞明勇董晓芬阿列克谢.苏林南京师范大学教育技术系江苏南京210097 南洋理工大学计算机工程学院新加坡639798

利用偏微分方程(PDE)进行曲面拟合是计算机图形学研究领域中的常用方法,该类方法通过选取适当的边界条件来构造PDE,用PDE的解来表示几何曲面.基于网格简化方法和离散曲面测地线计算等技术,提出一种从网格模型提取PDE曲面片边界条件曲线... 详细信息

利用偏微分方程(PDE)进行曲面拟合是计算机图形学研究领域中的常用方法,该类方法通过选取适当的边界条件来构造PDE,用PDE的解来表示几何曲面.基于网格简化方法和离散曲面测地线计算等技术,提出一种从网格模型提取PDE曲面片边界条件曲线的方法.首先,对复杂模型进行简化并分片处理;通过计算离散曲面的测地线为每个分片定义相应的PDE边界条件曲线,进而构造复杂模型的PDE拟合表面.最后,通过细分方法建立原模型的多分辨率表示.实验表明,该方法可以对具有不同几何复杂度的网格模型进行处理,产生具有细分连通性的多分辨网格模型.

关键词：偏微分方程曲面建模边界条件曲线曲面逼近多分辨率网络

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