scholarly journals RadViz: Improvements on Radial-Based Visualizations++

Informatics ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 16 ◽  
Author(s):  
Lucas de Carvalho de Carvalho Pagliosa ◽  
Alexandru C. Telea
Keyword(s):  
Real World ◽  
Visual Analytics ◽  
Design Space ◽  
High Dimensional Data ◽  
Added Value ◽  
High Dimensional ◽  
Interactive Exploration ◽  
Circular Design

RadViz is one of the few methods in Visual Analytics able to project high-dimensional data and explain formed structures in terms of data variables. However, RadViz methods have several limitations in terms of scalability in the number of variables, ambiguities created in the projection by the placement of variables along the circular design space, and ability to segregate similar instances into visual clusters. To address these limitations, we propose RadViz++, a set of techniques for interactive exploration of high-dimensional data using a RadViz-type metaphor. We demonstrate the added value of our method by comparing it with existing high-dimensional visualization methods, and also by analyzing a complex real-world dataset having over a hundred variables.

scholarly journals Unsupervised Learning for Large Scale Data: The ATHLOS Project

2021 ◽  
Author(s):  
Petros Barmpas ◽  
Sotiris Tasoulis ◽  
Aristidis G. Vrahatis ◽  
Panagiotis Anagnostou ◽  
Spiros Georgakopoulos ◽  
...  
Keyword(s):  
Unsupervised Learning ◽  
Real World ◽  
Large Scale ◽  
High Dimensional Data ◽  
Experimental Studies ◽  
Mixed Data ◽  
Categorical Variables ◽  
High Dimensional ◽  
Data Types ◽  
Unified Framework

1AbstractRecent technological advancements in various domains, such as the biomedical and health, offer a plethora of big data for analysis. Part of this data pool is the experimental studies that record various and several features for each instance. It creates datasets having very high dimensionality with mixed data types, with both numerical and categorical variables. On the other hand, unsupervised learning has shown to be able to assist in high-dimensional data, allowing the discovery of unknown patterns through clustering, visualization, dimensionality reduction, and in some cases, their combination. This work highlights unsupervised learning methodologies for large-scale, high-dimensional data, providing the potential of a unified framework that combines the knowledge retrieved from clustering and visualization. The main purpose is to uncover hidden patterns in a high-dimensional mixed dataset, which we achieve through our application in a complex, real-world dataset. The experimental analysis indicates the existence of notable information exposing the usefulness of the utilized methodological framework for similar high-dimensional and mixed, real-world applications.

scholarly journals UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data

2021 ◽  
pp. 1-1
Author(s):  
Mateus Espadoto ◽  
Gabriel Appleby ◽  
Ashley Suh ◽  
Dylan Cashman ◽  
Mingwei Li ◽  
...  
Keyword(s):  
Visual Analytics ◽  
High Dimensional Data ◽  
High Dimensional

scholarly journals Distortion-Guided Structure-Driven Interactive Exploration of High-Dimensional Data

2014 ◽  
Vol 33 (3) ◽  
pp. 101-110 ◽  
Author(s):  
S. Liu ◽  
B. Wang ◽  
P.-T. Bremer ◽  
V. Pascucci
Keyword(s):  
High Dimensional Data ◽  
High Dimensional ◽  
Interactive Exploration

scholarly journals Robust high-dimensional semiparametric regression using optimized differencing method applied to the vitamin B2 production data

2020 ◽  
Author(s):  
Mahdi Roozbeh ◽  
Monireh Maanavi ◽  
Saman Babaie-Kafaki
Keyword(s):  
Real World ◽  
High Dimensional Data ◽  
Semiparametric Regression ◽  
Ordinary Least Squares ◽  
Semiparametric Model ◽  
Nonlinear Integer Programming ◽  
High Dimensional ◽  
Optimization Approach ◽  
Integer Programming Problem ◽  
Data Set

Background and purpose: By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variables. In addition, classical methods are affected by the presence of outliers and collinearity.Methods: Nowadays, many real-world data sets carry structures of high-dimensional problems. To handle this problem, we used the least absolute shrinkage and selection operator (LASSO). Also, due to the flexibility and applicability of the semiparametric model in medical data, it can be used for modeling the genomic data. Motivated by these, here an improved robust approach in a high-dimensional data set was developed for the analysis of gene expression and prediction in the presence of outliers.Results: Among the common problems in regression analysis, there was the problem of outliers. In the regression concept, an outlier is a point that fails to follow the main linear pattern of the data. The ordinary least-squares estimator was found potentially sensitive to the outliers; this fact provided necessary motivations to investigate robust estimations. Generally, the robust regression is among the most popular problems in the statistics community. In the present study, the least trimmed squares (LTS) estimation was applied to overcome the outlier problem.Conclusions: We have proposed an optimization approach for semiparametric models to combat outliers in the data set. Especially, based on a penalization LASSO scheme, we have suggested a nonlinear integer programming problem as the semiparametric model which can be effectively solved by any evolutionary algorithm. We have also studied a real-world application related to the riboflavin production. The results showed that the proposed method was reasonably efficient in contrast to the LTS Method.

scholarly journals Quantitative and Qualitative Comparison of 2D and 3D Projection Techniques for High-Dimensional Data

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 239
Author(s):  
Zonglin Tian ◽  
Xiaorui Zhai ◽  
Gijs van Steenpaal ◽  
Lingyun Yu ◽  
Evanthia Dimara ◽  
...  
Keyword(s):  
Dimensional Space ◽  
High Dimensional Data ◽  
Visual Exploration ◽  
Added Value ◽  
High Dimensional ◽  
Qualitative Comparison ◽  
Projection Techniques ◽  
The One ◽  
Low Dimensional ◽  
2D And 3D

Projections are well-known techniques that help the visual exploration of high-dimensional data by creating depictions thereof in a low-dimensional space. While projections that target the 2D space have been studied in detail both quantitatively and qualitatively, 3D projections are far less well understood, with authors arguing both for and against the added-value of a third visual dimension. We fill this gap by first presenting a quantitative study that compares 2D and 3D projections along a rich selection of datasets, projection techniques, and quality metrics. To refine these insights, we conduct a qualitative study that compares the preference of users in exploring high-dimensional data using 2D vs. 3D projections, both without and with visual explanations. Our quantitative and qualitative findings indicate that, in general, 3D projections bring only limited added-value atop of the one provided by their 2D counterparts. However, certain 3D projection techniques can show more structure than their 2D counterparts, and can stimulate users to further exploration. All our datasets, source code, and measurements are made public for ease of replication and extension.

PolarViz: a discriminating visualization and visual analytics tool for high-dimensional data

2018 ◽  
Vol 35 (11) ◽  
pp. 1567-1582 ◽  
Author(s):  
Yan Chao Wang ◽  
Qian Zhang ◽  
Feng Lin ◽  
Chi Keong Goh ◽  
Hock Soon Seah
Keyword(s):  
Visual Analytics ◽  
High Dimensional Data ◽  
High Dimensional
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