Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE
We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One...
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doaj-3c500f5bb1a9401b83c2898c18ecb52c2020-11-25T00:39:02ZengMDPI AGSymmetry2073-89942019-01-0111110710.3390/sym11010107sym11010107Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNEMujtaba Husnain0Malik Muhammad Saad Missen1Shahzad Mumtaz2Muhammad Muzzamil Luqman3Mickaël Coustaty4Jean-Marc Ogier5Department of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Computer Science & IT, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanL3i, La Rochelle University, Avenue Michel Cŕepeau, 17000 La Rochelle, FranceL3i, La Rochelle University, Avenue Michel Cŕepeau, 17000 La Rochelle, FranceL3i, La Rochelle University, Avenue Michel Cŕepeau, 17000 La Rochelle, FranceWe applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset.http://www.mdpi.com/2073-8994/11/1/107dimension reductionmultidimensional information visualizationEuclidean distanceembedding algorithmspattern classification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mujtaba Husnain Malik Muhammad Saad Missen Shahzad Mumtaz Muhammad Muzzamil Luqman Mickaël Coustaty Jean-Marc Ogier |
spellingShingle |
Mujtaba Husnain Malik Muhammad Saad Missen Shahzad Mumtaz Muhammad Muzzamil Luqman Mickaël Coustaty Jean-Marc Ogier Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE Symmetry dimension reduction multidimensional information visualization Euclidean distance embedding algorithms pattern classification |
author_facet |
Mujtaba Husnain Malik Muhammad Saad Missen Shahzad Mumtaz Muhammad Muzzamil Luqman Mickaël Coustaty Jean-Marc Ogier |
author_sort |
Mujtaba Husnain |
title |
Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE |
title_short |
Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE |
title_full |
Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE |
title_fullStr |
Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE |
title_full_unstemmed |
Visualization of High-Dimensional Data by Pairwise Fusion Matrices Using t-SNE |
title_sort |
visualization of high-dimensional data by pairwise fusion matrices using t-sne |
publisher |
MDPI AG |
series |
Symmetry |
issn |
2073-8994 |
publishDate |
2019-01-01 |
description |
We applied t-distributed stochastic neighbor embedding (t-SNE) to visualize Urdu handwritten numerals (or digits). The data set used consists of 28 × 28 images of handwritten Urdu numerals. The data set was created by inviting authors from different categories of native Urdu speakers. One of the challenging and critical issues for the correct visualization of Urdu numerals is shape similarity between some of the digits. This issue was resolved using t-SNE, by exploiting local and global structures of the large data set at different scales. The global structure consists of geometrical features and local structure is the pixel-based information for each class of Urdu digits. We introduce a novel approach that allows the fusion of these two independent spaces using Euclidean pairwise distances in a highly organized and principled way. The fusion matrix embedded with t-SNE helps to locate each data point in a two (or three-) dimensional map in a very different way. Furthermore, our proposed approach focuses on preserving the local structure of the high-dimensional data while mapping to a low-dimensional plane. The visualizations produced by t-SNE outperformed other classical techniques like principal component analysis (PCA) and auto-encoders (AE) on our handwritten Urdu numeral dataset. |
topic |
dimension reduction multidimensional information visualization Euclidean distance embedding algorithms pattern classification |
url |
http://www.mdpi.com/2073-8994/11/1/107 |
work_keys_str_mv |
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