检索结果-内蒙古大学图书馆

High-dimensional data analysis with subspace comparison using matrix visualization

INFORMATION visualization 2019年第1期18卷 94-109页

作者： Wang, Junpeng Liu, Xiaotong Shen, Han-Wei Ohio State Univ 395 Dreese Labs2015 Neil Ave Columbus OH 43210 USA IBM Res Almaden San Jose CA USA

Due to the intricate relationship between different dimensions of high-dimensional data, subspace analysis is often conducted to decompose dimensions and give prominence to certain subsets of dimensions, i.e. subspaces. Exploring and comparing subspaces are important to reveal the underlying features of subspaces, as well as to portray the characteristics of individual dimensions. To date, most of the existing high-dimensional data exploration and analysis approaches rely on dimensionality reduction algorithms (e.g. principal component analysis and multi-dimensional scaling) to project high-dimensional data, or their subspaces, to two-dimensional space and employ scatterplots for visualization. However, the dimensionality reduction algorithms are sometimes difficult to fine-tune and scatterplots are not effective for comparative visualization, making subspace comparison hard to perform. In this article, we aggregate high-dimensional data or their subspaces by computing pair-wise distances between all data items and showing the distances with matrix visualizations to present the original high-dimensional data or subspaces. Our approach enables effective visual comparisons among subspaces, which allows users to further investigate the characteristics of individual dimensions by studying their behaviors in similar subspaces. Through subspace comparisons, we identify dominant, similar, and conforming dimensions in different subspace contexts of synthetic and real-world high-dimensional data sets. Additionally, we present a prototype that integrates parallel coordinates plot and matrix visualization for high-dimensional data exploration and incremental dimensionality analysis, which also allows users to further validate the dimension characterization results derived from the subspace comparisons.

关键词： High-dimensional data matrix visualization subspace comparison

来源：评论

学校读者我要写书评

暂无评论

Exploratory data analysis of interval-valued symbolic data with matrix visualization

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2014年 79卷 14-29页

作者： Kao, Chiun-How Nakano, Junji Shieh, Sheau-Hue Tien, Yin-Jing Wu, Han-Ming Yang, Chuan-Kai Chen, Chun-houh Natl Taiwan Univ Sci & Technol Dept Informat Management Taipei 10607 Taiwan Acad Sinica Inst Stat Sci Taipei 11529 Taiwan Inst Stat Math Tokyo 1068569 Japan Natl Taipei Univ Ctr Teacher Educ New Taipei City 23741 Taiwan Tamkang Univ Dept Math New Taipei City 25137 Taiwan

Symbolic data analysis (SDA) has gained popularity over the past few years because of its potential for handling data having a dependent and hierarchical nature. Amongst many methods for analyzing symbolic data, exploratory data analysis (EDA: Tukey, 1977) with graphical presentation is an important one. Recent developments of graphical and visualization tools for SDA include zoom star, closed shapes, and parallel-coordinate-plots. Other studies project high dimensional symbolic data into lower dimensional spaces using symbolic data versions of principal component analysis, multidimensional scaling, and self-organizing maps. Most graphical and visualization approaches for exploring symbolic data structure inherit the advantages of their counterparts for conventional (non-symbolic) data, but also their disadvantages. Here we introduce matrix visualization (MV) for visualizing and clustering symbolic data using interval-valued symbolic data as an example;it is by far the most popular symbolic data type in the literature and the most commonly encountered one in practice. Many MV techniques for visualizing and clustering conventional data are converted to symbolic data, and several techniques are newly developed for symbolic data. Various examples of data with simple to complex structures are brought in to illustrate the proposed methods. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Symbolic data analysis Interval-valued data matrix visualization Generalized association plots Proximity matrix Exploratory data analysis EDA

来源：评论

学校读者我要写书评

暂无评论

Animatrix: A matrix-Based visualization of Software Evolution 2

AniMatrix: A Matrix-Based Visualization of Software Evolutio...

引用

2nd IEEE Working Conference on Software visualization (VISSOFT)

作者： Rufiange, Sebastien Melancon, Guy Univ Bordeaux INRIA Bordeaux Sud Ouest UMR 5800 LaBRI Bordeaux France

ISBN: (纸本)9780769553054

Software designs are ever changing to adapt to new environments and requirements. Tracking and understanding changes in modules and relationships in a software project is difficult, but even more so when the software goes through several types of changes. The typical complexity and size of software also makes it harder to grasp software evolution patterns. In this paper, we present an interactive matrix-based visualization technique that, combined with animation, depicts how software designs evolve. For example, it shows which new modules and couplings are added and removed over time. Our generic visualization supports dynamic and weighted digraphs and is applied in the context of software evolution. Analyzing source code changes is important to determine the software's structural organization and identify quality issues over time. To demonstrate our approach, we explore open-source repositories and discuss some of our findings regarding these evolving software designs.

关键词： matrix visualization network visualization software design software evolution software visualization

来源：评论

学校读者我要写书评

暂无评论

Comparing and Exploring High-Dimensional Data with Dimensionality Reduction Algorithms and matrix visualizations 20

Comparing and Exploring High-Dimensional Data with Dimension...

引用

Working Conference on Advanced Visual Interfaces (AVI)

作者： Cutura, Rene Aupetit, Michael Fekete, Jean-Daniel Sedlmair, Michael Vienna Univ Technol Vienna Austria Hamad Bin Khalifa Univ Qatar Comp Res Inst Doha Qatar Univ Paris Saclay LRI INRIA CNRS Orsay France Univ Stuttgart Stuttgart Germany

ISBN: (纸本)9781450375351

We propose Compadre, a tool for visual analysis for comparing distances of high-dimensional (HD) data and their low-dimensional projections. At the heart is a matrix visualization to represent the discrepancy between distance matrices, linked side-by-side with 2D scatterplot projections of the data. Using different examples and datasets, we illustrate how this approach fosters (1) evaluating dimensionality reduction techniques w.r.t. how well they project the HD data, (2) comparing them to each other side-by-side, and (3) evaluate important data features through subspace comparison. We also present a case study, in which we analyze IEEE VIS authors from 1990 to 2018, and gain new insights on the relationships between coauthors, citations, and keywords. The coauthors are projected as accurately with UMAP as with t-SNE but the projections show different insights. The structure of the citation subspace is very different from the coauthor subspace. The keyword subspace is noisy yet consistent among the three IEEE VIS sub-conferences.

关键词： Dimensionality Reduction matrix visualization Visual Comparison High-Dimensional Data Subspace Analysis

来源：评论

学校读者我要写书评

暂无评论

Responsive matrix Cells: A Focus plus Context Approach for Exploring and Editing Multivariate Graphs

引用

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第2期27卷 1644-1654页

作者： Horak, Tom Berger, Philip Schumann, Heidrun Dachselt, Raimund Tominski, Christian Tech Univ Dresden Interact Media Lab Dresden Germany Univ Rostock Inst Visual & Anal Comp Rostock Germany

matrix visualizations are a useful tool to provide a general overview of a graph's structure. For multivariate graphs, a remaining challenge is to cope with the attributes that are associated with nodes and edges. Addressing this challenge, we propose responsive matrix cells as a focus+context approach for embedding additional interactive views into a matrix. Responsive matrix cells are local zoomable regions of interest that provide auxiliary data exploration and editing facilities for multivariate graphs. They behave responsively by adapting their visual contents to the cell location, the available display space, and the user task. Responsive matrix cells enable users to reveal details about the graph, compare node and edge attributes, and edit data values directly in a matrix without resorting to external views or tools. We report the general design considerations for responsive matrix cells covering the visual and interactive means necessary to support a seamless data exploration and editing. Responsive matrix cells have been implemented in a web-based prototype based on which we demonstrate the utility of our approach. We describe a walk-through for the use case of analyzing a graph of soccer players and report on insights from a preliminary user feedback session.

关键词： Multivariate graph visualization matrix visualization focus plus context embedded visualizations responsive visualization graph editing

来源：评论

学校读者我要写书评

暂无评论

Diagnosing Ensemble Few-Shot Classifiers

引用

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2022年第9期28卷 3292-3306页

作者： Yang, Weikai Ye, Xi Zhang, Xingxing Xiao, Lanxi Xia, Jiazhi Wang, Zhongyuan Zhu, Jun Pfister, Hanspeter Liu, Shixia Tsinghua Univ Sch Software BNRist Beijing 100084 Peoples R China Univ Texas Austin Austin TX 78712 USA Tsinghua Univ Dept Comp Sci & Technol BNRist Beijing 100084 Peoples R China Tsinghua Univ Acad Arts & Design Beijing 100084 Peoples R China Cent South Univ Changsha 410017 Hunan Peoples R China Kuaishou Technol Co Ltd Beijing 100085 Peoples R China Harvard Univ Cambridge MA 02138 USA

The base learners and labeled samples (shots) in an ensemble few-shot classifier greatly affect the model performance. When the performance is not satisfactory, it is usually difficult to understand the underlying causes and make improvements. To tackle this issue, we propose a visual analysis method, FSLDiagnotor. Given a set of base learners and a collection of samples with a few shots, we consider two problems: 1) finding a subset of base learners that well predict the sample collections;and 2) replacing the low-quality shots with more representative ones to adequately represent the sample collections. We formulate both problems as sparse subset selection and develop two selection algorithms to recommend appropriate learners and shots, respectively. A matrix visualization and a scatterplot are combined to explain the recommended learners and shots in context and facilitate users in adjusting them. Based on the adjustment, the algorithm updates the recommendation results for another round of improvement. Two case studies are conducted to demonstrate that FSLDiagnotor helps build a few-shot classifier efficiently and increases the accuracy by 12% and 21%, respectively.

关键词： visualization Feature extraction Predictive models Training Analytical models Data models Behavioral sciences Few-shot learning ensemble model subset selection matrix visualization scatterplot

来源：评论

学校读者我要写书评

暂无评论

NodeTrix: A hybrid visualization of social networks

引用

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2007年第6期13卷 1302-1309页

作者： Henry, Nathalie Fekete, Jean-Daniel McGuffin, Michael J. Univ Paris Sud INRIA Futurs Orsay France Univ Toronto Ontario Canc Inst Toronto ON Canada

The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NocleTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship clataset to illustrate the capabilities of NocleTrix as both an exploration tool and an effective means of communicating results.

关键词： network visualization matrix visualization hybrid visualization aggreiation interaction

来源：评论

学校读者我要写书评

暂无评论

Recut: A Seriation Algorithm Balancing Smooth Display and Aggregated Features

引用

FUNDAMENTA INFORMATICAE 2016年第3期146卷 293-304页

作者： Qi, Ji Ohsawa, Yukio Univ Tokyo Sch Engn Dept Syst Innovat Bunkyo Ku 7 Chome 3-1 Hongo Tokyo Japan

Previous seriation algorithms are confronted with a balance problem. Some approaches provide permutations with perfect wholeness, where matrix rows/columns are associated with increasing or decreasing gradient. However, this smooth permutation may lead to the blurred representation of the data structure, such as clustering structures and detailed structures inside clusters. Some other approaches indicate these structures well by tighter aggregating similar rows/columns, but this aggregation is alway at the cost of losing necessary coherence of the matrix rows/columns. In this paper, we introduce a seriation algorithm that aims at balancing the smoothness of the permutation and the clarity of the matrix structure. The permutation algorithm greedily and recursively replaces high-dissimilar object pairs with low-dissimilar ones, and the optimization algorithm searches the global optimizing solution by applying the simulated annealing algorithm. A comparison study shows both empirical and statistical evidence that Recut can provide more accurate and visually appropriate permutation by considering the balance problem.

关键词： Seriation matrix Reordering matrix visualization Data visualization Data Analysis

来源：评论

学校读者我要写书评

暂无评论

Sybil attack detection through global topology pattern visualization

引用

INFORMATION visualization 2011年第1期10卷 32-46页

作者： Lu, Aidong Wang, Weichao Dnyate, Abhishek Hu, Xianlin Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA Univ N Carolina Dept Software & Informat Syst Charlotte NC 28223 USA

We present a robust intrusion detection approach for wireless networks based on a new multi-matrix visualization method with a set of pattern generation, evaluation, organization and interaction functions. Our approach concentrates on assisting users to analyze statistical network topology patterns that could expose significant attack features. Specifically, we investigate Sybil attacks that have severe impacts on the fundamental operations of wireless networks. We have analyzed the features of network topologies under various Sybil attacks and, consequently, designed several matrix reordering algorithms to generate statistical patterns. These topology patterns are automatically evaluated and classified through the measured structural similarities to the signature attack patterns. We have also designed a new time-series analysis method to identify attack durations with a time histogram generation and an automatic segmentation method. To handle complex Sybil attacks, we have integrated our pattern generation, evaluation and organization methods to construct a prototype detection system, in which specialized interaction functions are provided to assist the analysis and comparison of network data. Simulation results show that this approach can effectively locate Sybil attacks under different combinations of network parameters. Our multi-matrix visualization method provides a flexible framework to handle the intricacies and implications from building a complex visual analytics system, which can be extended to defend against a wide range of attacks. Information visualization (2011) 10, 32-46. doi: 10.1057/ivs.2010.1;published online 2 September 2010

关键词： matrix visualization matrix reordering security visualization intrusion detection Sybil attack

来源：评论

学校读者我要写书评

暂无评论

Magnostics: Image-based Search of Interesting matrix Views for Guided Network Exploration

引用

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2017年第1期23卷 31-40页

作者： Behrisch, Michael Bach, Benjamin Hund, Michael Delz, Michael von Rueden, Laura Fekete, Jean-Daniel Schreck, Tobias Univ Konstanz Constance Germany Microsoft Res Inria Joint Ctr Saclay France Capgemini Paris France Rhein Westfal TH Aachen Aachen Germany Inria Saclay France Graz Univ Technol Graz Austria

In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce matrix Diagnostics (or MAGNOSTICS), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. MAGNOSTICS can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for MAGNOSTICS and demonstrate their application in two scenarios;exploring a large collection of matrices and analyzing temporal networks.

关键词： matrix visualization Visual Quality Measures Quality Metrics Feature Detection/Selection Relational Data

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：