A new supervised t-SNE with dissimilarity measure for effective data visualization and classification
Hajderanj, L. Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1145/3328833.3328853 Abstract/SummaryIn this paper, a new version of the Supervised t- Stochastic Neighbor Embedding (S-tSNE) algorithm is proposed which introduces the use of a dissimilarity measure related to class information. The proposed S-tSNE can be applied in any high dimensional dataset for visualization 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- Stochastic Neighbor Embedding(t-SNE) method. The results from k-nearest neighbors (k-NN) classification model which used the lower dimension space generated by the new S-tSNE method showed more than 20% improvement on average in accuracy in all the three datasets compared with the 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.
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