Fabrice Tshimanga<p>3/n</p><p>We thus decided to use the General Distance Measure to compute pairwise similarities between our 172 subjects, and obtained a matrix, which as math savy people know, is also the description of a network (an "adjacency matrix" for a "weighted undirected graph").<br>The problem was then to find cliques, communities or clusters of similar patients in such a network, and we used spectral clustering.<br>Spectral clustering is a family of techniques that use spectra of matrices describing networks, i.e. use eigenvalues of matrices to understand the structure of those networks.</p><p><a href="https://neuromatch.social/tags/spectralanalysis" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spectralanalysis</span></a> <a href="https://neuromatch.social/tags/spectralclustering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>spectralclustering</span></a> <a href="https://neuromatch.social/tags/clustering" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>clustering</span></a> <a href="https://neuromatch.social/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a></p>