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 />
The matrices W <strong>and</strong> H are found by maximizing<br />
L(W, H) =<br />
N<br />
i=1<br />
N<br />
[xijlog(WHij) − (WHij)] (14)<br />
i=j<br />
This is the log-Likelihood from a model in which xij has a<br />
Poisson distribution with mean (WB)ij<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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