Comparing and Exploring High|Dimensional Data with ...

How to Create Engaging Multidimensional Data Visualizations

High dimensional data visualization techniques offer a way to explore and understand complex, multidimensional data sets. In this article ...

Discovering Patterns in High-Dimensional Data

Facebook has released HiPlot, a data visualization tool that can help researchers discover correlations and patterns in high-dimensional data.

A comparison of optimisation algorithms for high-dimensional ...

E.A. Baltz and P. Gondolo, Markov chain Monte Carlo exploration of minimal supergravity with implications for dark matter, JHEP 10 (2004) 052 [ ...

Exploring High-Dimensional Data by Pointwise Filtering of Low ...

Dimensionality reductions are a class of unsupervised learning algorithms that aim to find a lower-dimensional embedding for a high-dimensional dataset ...

High Dimensional Data Visualization: Advances and Challenges

Optimizing Star-Coordinate Visualization Method for Effective Interactive Cluster Exploration on Big Data. ... Dimensionality reduction: A comparative Review.

Introduction to T-Sne for High Dimensional Visualization

... dimensional data can be illustrated by comparing PCA and t-SNE data transformations. ... explore complex, high dimensional data. T-SNE is useful for ...

EpiVECS: exploring spatiotemporal epidemiological data using ...

... higher quality clusters and a better low-dimensional cluster representation when compared with self-organizing maps. There is no standard ...

GBD Compare - VizHub

GBD Compare. GBD Compare. Help. Provide feedbackAbout the toolAbout Global Burden ... Explore data, understand trends, and learn which countries have made ...

distributions - Comparing high dimensional samples - Cross Validated

Explore Teams. Teams. Q&A for work. Connect and share knowledge ... Tests for high dimensional data? Or do you find shortcomings in my ...

What Is Data Visualization? Definition & Examples | Tableau

Interactively explore opportunities. Visualize patterns and relationships ... As the “age of Big Data” kicks into high gear, visualization is an ...

A practical guide to dimensionality reduction techniques - Hex

... data. It does this by comparing the similarities between data points in both the original high-dimensional space and a lower-dimensional space.

High-dimensional Data Cubes - VLDB Endowment

dimensions compared to traditional data cubes. PVLDB Reference Format ... This paper explores how such high-dimensional data cubes can be made a ...

278-2012: Hyperslicing with SAS®

COMPARE X= variable | (variable-1 ... ... Hyperslice is an effective technique to explore high dimensional data and thus, through this paper, we have shown.

Dimension Projection Matrix/Tree: Interactive Subspace Visual ...

While the tree nodes visualize the subspaces of dimensions or subsets of the data items under exploration, the matrix nodes enable cross-comparison between ...

Improving Dimensionality Reduction Projections for Data Visualization

... exploring high-dimensional data. Many visualization systems leverage ... Overview and comparative study of dimensionality reduction techniques for ...

Interactively Exploring Embedding Quality with TRACE - arXiv

To address these limitations, we developed a Two-dimensional Representation Analysis and Comparison Engine (TRACE) with a flexible and ...

A Tool for Visually Comparing Dimensionality Reduction Algorithms

Dimensionality reduction (DR) is a widely used approach to visualize high- dimensional data. ... The Component Plane view [11] helps to explore how dimensions are ...

Curse of dimensionality - Wikipedia

The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in ...

Our World in Data

Explore our data. Featured data from our collection of 13,222 interactive ... high living standards. We can, therefore, think of child mortality as a ...

Dimensionality reduction for exploration and curation of datasets

... high-dimensional data in a lower-dimensional space. It is particularly effective in preserving both the local and global structure of the data.