Self-Organizing Maps, Principal Components and Non-negative ...
Self-Organizing Maps, Principal Components and Non-negative ... Self-Organizing Maps, Principal Components and Non-negative ...
Self Organizing Maps Principal Components, Curves and Surfaces Non-negative Matrix Factorization Table of Contents 1 Self Organizing Maps 2 Principal Components, Curves and Surfaces Principal Components Principal Curves Spectral Clustering 3 Non-negative Matrix Factorization Karoline Geissler Self-Organizing Maps, Principal Components and Non-negative M
Self Organizing Maps Principal Components, Curves and Surfaces Non-negative Matrix Factorization The simplest version of the SOM Other version of the SOM Example Reconstruction Error The method can be viewed as a version of K-means clustering. The prototypes lie in a one- or two- dimensional manifold. The resulting manifold is referred to as a constrained topological map. The original high dimensional observation can be mapped down onto a two-dimensional coordinate system. Karoline Geissler Self-Organizing Maps, Principal Components and Non-negative M
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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 />
Table of Contents<br />
1 <strong>Self</strong> <strong>Organizing</strong> <strong>Maps</strong><br />
2 <strong>Principal</strong> <strong>Components</strong>, Curves <strong>and</strong> Surfaces<br />
<strong>Principal</strong> <strong>Components</strong><br />
<strong>Principal</strong> Curves<br />
Spectral Clustering<br />
3 <strong>Non</strong>-<strong>negative</strong> Matrix Factorization<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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