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arXiv · 2001.01958

A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy

Alberto García-González·Antonio Huerta·Sergio Zlotnik·Pedro Díez
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Abstract

Methodologies for multidimensionality reduction aim at discovering low-dimensional manifolds where data ranges. Principal Component Analysis (PCA) is very effective if data have linear structure. But fails in identifying a possible dimensionality reduction if data belong to a nonlinear low-dimensional manifold. For nonlinear dimensionality reduction, kernel Principal Component Analysis (kPCA) is appreciated because of its simplicity and ease implementation. The paper provides a concise review of PCA and kPCA main ideas, trying to collect in a single document aspects that are often dispersed. Moreover, a strategy to map back the reduced dimension into the original high dimensional space is also devised, based on the minimization of a discrepancy functional.

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Alberto García-González, Antonio Huerta, Sergio Zlotnik, Pedro Díez. 2020-01-07. A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy. https://arxiv.org/abs/2001.01958

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