scholarly journals Retrieval of high-dimensional visual data: current state, trends and challenges ahead

2013 ◽  
Vol 69 (2) ◽  
pp. 539-567 ◽  
Author(s):  
Antonio Foncubierta-Rodríguez ◽  
Henning Müller ◽  
Adrien Depeursinge
Keyword(s):  
High Dimensional ◽  
Visual Data ◽  
Current State

scholarly journals Depicting More Information in Enriched Squarified Treemaps with Layered Glyphs

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 123
Author(s):  
Anderson Gregório Marques Soares ◽  
Elvis Thermo Carvalho Miranda ◽  
Rodrigo Santos do Amor Divino Lima ◽  
Carlos Gustavo Resque dos Santos ◽  
Bianchi Serique Meiguins
Keyword(s):  
Information Visualization ◽  
Data Structures ◽  
User Study ◽  
Relevant Information ◽  
Data Representation ◽  
High Dimensional ◽  
Hierarchical Data ◽  
Visual Data ◽  
Visual Variable ◽  
Data Clusters

The Treemap is one of the most relevant information visualization (InfoVis) techniques to support the analysis of large hierarchical data structures or data clusters. Despite that, Treemap still presents some challenges for data representation, such as the few options for visual data mappings and the inability to represent zero and negative values. Additionally, visualizing high dimensional data requires many hierarchies, which can impair data visualization. Thus, this paper proposes to add layered glyphs to Treemap’s items to mitigate these issues. Layered glyphs are composed of N partially visible layers, and each layer maps one data dimension to a visual variable. Since the area of the upper layers is always smaller than the bottom ones, the layers can be stacked to compose a multidimensional glyph. To validate this proposal, we conducted a user study to compare three scenarios of visual data mappings for Treemaps: only Glyphs (G), Glyphs and Hierarchy (GH), and only Hierarchy (H). Thirty-six volunteers with a background in InfoVis techniques, organized into three groups of twelve (one group per scenario), performed 8 InfoVis tasks using only one of the proposed scenarios. The results point that scenario GH presented the best accuracy while having a task-solving time similar to scenario H, which suggests that representing more data in Treemaps with layered glyphs enriched the Treemap visualization capabilities without impairing the data readability.

scholarly journals Enhancing Nearest Neighbor Based Entropy Estimator for High Dimensional Distributions via Bootstrapping Local Ellipsoid

2020 ◽  
Vol 34 (04) ◽  
pp. 5013-5020
Author(s):  
Chien Lu ◽  
Jaakko Peltonen
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Testing Procedure ◽  
High Dimensionality ◽  
High Dimensional ◽  
Acceptance Testing ◽  
Volume Correction ◽  
Current State ◽  
Entropy Estimator ◽  
Random Samples

An ellipsoid-based, improved kNN entropy estimator based on random samples of distribution for high dimensionality is developed. We argue that the inaccuracy of the classical kNN estimator in high dimensional spaces results from the local uniformity assumption and the proposed method mitigates the local uniformity assumption by two crucial extensions, a local ellipsoid-based volume correction and a correction acceptance testing procedure. Relevant theoretical contributions are provided and several experiments from simple to complicated cases have shown that the proposed estimator can effectively reduce the bias especially in high dimensionalities, outperforming current state of the art alternative estimators.

scholarly journals Scalable Multiplex Network Embedding

2018 ◽  
Author(s):  
Hongming Zhang ◽  
Liwei Qiu ◽  
Lingling Yi ◽  
Yangqiu Song
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
High Dimensional ◽  
Network Embedding ◽  
Current State ◽  
Multiplex Network ◽  
Different Types ◽  
Comparable Performance ◽  
Combine Information ◽  
Real World Problems

Network embedding has been proven to be helpful for many real-world problems. In this paper, we present a scalable multiplex network embedding model to represent information of multi-type relations into a unified embedding space. To combine information of different types of relations while maintaining their distinctive properties, for each node, we propose one high-dimensional common embedding and a lower-dimensional additional embedding for each type of relation. Then multiple relations can be learned jointly based on a unified network embedding model. We conduct experiments on two tasks: link prediction and node classification using six different multiplex networks. On both tasks, our model achieved better or comparable performance compared to current state-of-the-art models with less memory use.

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