Principal Manifolds for Data Visualization and Dimension Reduction

Many alternative definitions were introduced (see, for example, [34]) in order to improve the situation and to allow the construction of principal curves (manifolds) for a distribution of points with several finite first moments.

Principal Manifolds for Data Visualization and Dimension Reduction

The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described. Presentation of algorithms is supplemented by case studies. The volume ends with a tutorial PCA deciphers genome.

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