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

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_center" colspan="3">Coefficient of variation per group</td></tr><tr class="thead"><td class="align_left"><i>S</i><sub>H</sub></td><td class="align_center"><i>S</i><sub>PD</sub></td><td class="align_center"><i>S</i><sub>DBS</sub></td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">0.21 ± 0.14</td><td class="align_center">0.24 ± 0.15</td><td class="align_center">0.30 ± 0.21</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Overall mean coefficient of variation (1 = 100%) among each repetition of the subject.</div>

Computational and Mathematical Methods in Medicine

tab1

Table 1

Table 1: On the Use of <i>t</i>-Distributed Stochastic Neighbor Embedding for Data Visualization and Classification of Individuals with Parkinson’s Disease 

Computational and Mathematical Methods in Medicine

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

Table 1