High Dimensional Visual Data Classification

2007 ◽  
pp. 25-34 ◽  
Author(s):  
François Poulet
Keyword(s):  
Data Classification ◽  
High Dimensional ◽  
Visual Data

Visual data classification in post-event building reconnaissance

2018 ◽  
Vol 155 ◽  
pp. 16-24 ◽  
Author(s):  
Chul Min Yeum ◽  
Shirley J. Dyke ◽  
Julio Ramirez
Keyword(s):  
Data Classification ◽  
Visual Data

The IANET Hardware Accelerator for Audio and Visual Data Classification

2020 ◽  
Author(s):  
Rohini J. Gillela ◽  
Amlan Ganguly ◽  
Dorin Patru ◽  
Mark A. Indovina
Keyword(s):  
Data Classification ◽  
Hardware Accelerator ◽  
Visual Data

A modular eigen subspace scheme for high-dimensional data classification

2004 ◽  
Vol 20 (7) ◽  
pp. 1131-1143 ◽  
Author(s):  
Yang-Lang Chang ◽  
Chin-Chuan Han ◽  
Fan-Di Jou ◽  
Kuo-Chin Fan ◽  
K.S. Chen ◽  
...  
Keyword(s):  
High Dimensional Data ◽  
Data Classification ◽  
High Dimensional

Multiple Bayesian discriminant functions for high-dimensional massive data classification

2016 ◽  
Vol 31 (2) ◽  
pp. 465-501 ◽  
Author(s):  
Jianfei Zhang ◽  
Shengrui Wang ◽  
Lifei Chen ◽  
Patrick Gallinari
Keyword(s):  
Data Classification ◽  
High Dimensional ◽  
Massive Data ◽  
Discriminant Functions

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.

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