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A co-analysis framework for exploring multivariate scientific data

Visual Informatics 2018年第4期2卷 254-263页

作者： Xiangyang He Yubo Tao Qirui Wang Hai Lin State Key Lab of CAD&CG Zhejiang UniversityChina

In complex multivariate data sets,different features usually include diverse associations with different variables,and different variables are associated within different regions.Therefore,exploring the associations between variables and voxels locally becomes necessary to better understand the underlying phenomena.In this paper,we propose a co-analysis framework based on biclusters,which are two subsets of variables and voxels with close scalar-value relationships,to guide the process of visually exploring multivariate data.We first automatically extract all meaningful biclusters,each of which only contains voxels with a similar scalar-value pattern over a subset of variables.These biclusters are organized according to their variable sets,and biclusters in each variable set are further grouped by a similarity metric to reduce redundancy and support diversity during visual exploration.Biclusters are visually represented in coordinated views to facilitate interactive exploration of multivariate data from the similarity between biclusters and the correlation of scalar values with different variables.Experiments on several representative multivariate scientific data sets demonstrate the effectiveness of our framework in exploring local relationships among variables,biclusters and scalar values in the data.

关键词： multivariate data Bicluster Local association

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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 datasets.Many approaches address the issue of unlabelled datasets by tightly coupling machine learning algorithms with interactive visualisations.Using appropriate techniques,analysts can play an active role in a highly interactive and iterative machine learning process to label the dataset and create meaningful partitions.While this principle has been implemented either for unsupervised,semi-supervised,or supervised machine learning tasks,the combination of all three methodologies remains challenging.In 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)system.The 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 built.In the workflow,the analyst can label interesting patterns or outliers in a semi-supervised process supported by active learning.Once 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 dataset.Using 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 classifier.The tool empowers the analyst with interactive visualisations including scatterplots,parallel coordinates,similarity maps for records,and a ne

关键词： Labelling Clustering Classification Active learning multivariate data Visualisation

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Consistency of the Estimator of Cumulative Hazard Function in Estimated Pseudo-Partial-Likelihood Approach

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Wuhan University Journal of Natural Sciences 2009年第5期14卷 373-377页

作者： LIU Yanyan,YUAN Zhongshang School of Mathematics and Statistics,Wuhan University,Wuhan 430072,Hubei,China School of Mathematics and Statistics Wuhan University Wuhan China

For multivariate failure time with auxiliary covariate information, an estimated pseudo-partial-likelihood estimator under the marginal hazard model with distinguishable baseline hazard has been proposed. However, the asymptotic properties of the corresponding estimated cumulative hazard function have not been studied. In this paper, based on counting process martingale, we use the continuous mapping theorem and Lenglart inequality and prove the consistency of the estimated cumulative hazard function in estimated pseudo-partial-likelihood approach.

关键词： cumulative hazard function pseudo-partial-likeli- hood consistency auxiliary covariate multivariate data validation sample

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