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A General Framework for Dimensionality-Reducing Data Visualization Mapping

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In recent years, a wealth of dimension-reduction techniques for data visualization and preprocessing has been established. Nonparametric methods require additional effort for out-of-sample extensions, because they provide only a mapping of a given finite set of points. In this letter, we propose a general view on nonparametric dimension reduction based on the concept of cost functions and properties of the data. Based on this general principle, we transfer nonparametric dimension reduction to explicit mappings of the data manifold such that direct out-of-sample extensions become possible. Furthermore, this concept offers the possibility of investigating the generalization ability of data visualization to new data points. We demonstrate the approach based on a simple global linear mapping, as well as prototype-based local linear mappings. In addition, we can bias the functional form according to given auxiliary information. This leads to explicit supervised visualization mappings with discriminative properties comparable to state-of-the-art approaches.
Title: A General Framework for Dimensionality-Reducing Data Visualization Mapping
Description:
In recent years, a wealth of dimension-reduction techniques for data visualization and preprocessing has been established.
Nonparametric methods require additional effort for out-of-sample extensions, because they provide only a mapping of a given finite set of points.
In this letter, we propose a general view on nonparametric dimension reduction based on the concept of cost functions and properties of the data.
Based on this general principle, we transfer nonparametric dimension reduction to explicit mappings of the data manifold such that direct out-of-sample extensions become possible.
Furthermore, this concept offers the possibility of investigating the generalization ability of data visualization to new data points.
We demonstrate the approach based on a simple global linear mapping, as well as prototype-based local linear mappings.
In addition, we can bias the functional form according to given auxiliary information.
This leads to explicit supervised visualization mappings with discriminative properties comparable to state-of-the-art approaches.

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