Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences· 2016Q1· Review
Principal component analysis: a review and recent developments
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- 2016year
Short summary
Principal Component Analysis (PCA) is a technique that reduces the dimensionality of large datasets by creating new, uncorrelated variables that maximize variance, thereby increasing interpretability while minimizing information loss.
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Key points
- PCA reduces dataset dimensionality by creating new, uncorrelated variables.
- These new variables are designed to successively maximize the variance captured from the original data.
- The process involves solving an eigenvalue/eigenvector problem.
- PCA is an adaptive technique, with variants tailored for different data types and structures.
AI-generated from the title and abstract; the full text is not read.
Abstract
Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
The authors' abstract, as published at the source. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 2016 · DOI ↗
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Field: Analytical Chemistry
Analytical ChemistryChemistry