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 />
<strong>Principal</strong> <strong>Components</strong><br />
<strong>Principal</strong> <strong>Components</strong><br />
<strong>Principal</strong> Curves<br />
Spectral Clustering<br />
provide a sequence of best linear approximations to the given<br />
data in R p , of all ranks q ≤ p<br />
observations x1, ..., xN <strong>and</strong> rank - q linear model<br />
µ ... location vector in R p<br />
f (λ) = µ + Vqλ (3)<br />
Vq ... p × q matrix with q orthogonal unit vectors as columns<br />
λ ... q vector of parameters<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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