Self-Organizing Maps, Principal Components and Non-negative ...
Self-Organizing Maps, Principal Components and Non-negative ...
Self-Organizing Maps, Principal Components and Non-negative ...
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<strong>Self</strong> <strong>Organizing</strong> <strong>Maps</strong><br />
<strong>Principal</strong> <strong>Components</strong>, Curves <strong>and</strong> Surfaces<br />
<strong>Non</strong>-<strong>negative</strong> Matrix Factorization<br />
Unnormalized graph Laplacian<br />
<strong>Principal</strong> <strong>Components</strong><br />
<strong>Principal</strong> Curves<br />
Spectral Clustering<br />
A fully connected graph includes all pairwise edges with<br />
weights wii ′ = sii ′<br />
adjacency matrix... matrix of edge weights W = {wii ′}<br />
G... diagonal matrix with diagonal elements gi = <br />
i<br />
sum of the weights connected to it)<br />
unnormalized graph Laplacian<br />
′ wii ′ (the<br />
L = G − W (10)<br />
Karoline Geissler <strong>Self</strong>-<strong>Organizing</strong> <strong>Maps</strong>, <strong>Principal</strong> <strong>Components</strong> <strong>and</strong> <strong>Non</strong>-<strong>negative</strong> M
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