Vlachos, M., Domeniconi, C., Gunopulos, D., Kollios, G.Journal of machine learning research, 9(Nov), pp.2579-2605. In Advances in neural information processing systems (pp. Laplacian eigenmaps and spectral techniques for embedding and clustering. Nonlinear dimensionality reduction by locally linear embedding. A global geometric framework for nonlinear dimensionality reduction. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(1), pp.39-54. Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data. In International conference on database theory (pp. On the surprising behavior of distance metrics in high dimensional space. Overfitting in linear feature extraction for classification of high-dimensional image data. 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.
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. 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. In this study, the S-tSNE is applied to three datasets MNIST, Chest x-ray, and SEER Breast Cancer. The proposed S-tSNE can be applied in any high dimensional dataset for visualization or as a feature extraction for classification problems. In 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.
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