On the Use of t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease

<div>Visualization of projected data onto a lower dimensional space (step 3 in Figure <a href="../fig3/">3</a>). The visualizations are scatter plots representing dimensionless scores of the projection of high-dimensional feature vectors onto the first (x-axis) against the second (y-axis) estimated components. (a) The data are from Task 1, which is the movement of finger taps. Triangles represent SH, asterisks SPD, and circles SDBS. (b) The projections of PCA technique, (c) projections of Sammon’s mapping, and (d) t-SNE map.</div>

Computational and Mathematical Methods in Medicine

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Figure 5: On the Use of t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease

Figure 5 | On the Use of t-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease 