A New Supervised t-SNE with Dissimilarity Measure for Effective Data Visualization and Classification

Conference paper

Hajderanj, L, Weheliye, I and Chen, D (2019). A New Supervised t-SNE with Dissimilarity Measure for Effective Data Visualization and Classification. 2019 8th International Conference on Software and Information Engineering. Cairo 09 - 12 Apr 2019
AuthorsHajderanj, L, Weheliye, I and Chen, D
TypeConference paper
Abstract

In this paper, a new version of supervised t-SNE algorithm is proposed which introduces using a dissimilarity measure elated with class information. The proposed S-tSNE can be applied in any high dimensional dataset for visualisation
or as a feature extraction for classification problems. In this study the S-tSNE is applied to three datasets MNIST, Chest x-ray and SEER Breast Cancer. The two-dimensional data generated by the S-tSNE showed better visualization and an improvement in terms of classification accuracy in comparison to the original t-SNE method. The results from k-NN classification models which used the lower dimension space generated by the new S-tSNE methods showed more than 20% accuracy improvement in all the three datasets compared with t-SNE method. In addition, the classification accuracy using the S-tSNE for feature extraction was even higher than classification accuracy obtained from the original high-dimensional data.

Year2019
Accepted author manuscript
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Print09 Apr 2019
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Deposited20 Mar 2019
Accepted11 Mar 2019
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https://openresearch.lsbu.ac.uk/item/866zq

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2019 03 19 IE043.pdf
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