# principal component analysis

- principal component analysis
**principal component analysis ( PCA)**
A mathematical tool used to reduce the number of variables while retaining the original variability of the data The first principal component accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible. In interest rate risk analysis, PCA is applied to define non-parallel yield curve sifts to model. The number of variables is equal to the number of points on the yield curve, the first principal component is the rate level, the second is the twist or rotation of the yield curve around a pivot point and the third is the change in curvature or " bow" in the yield curve. __American Banker Glossary__
*Financial and business terms.
2012.*

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**Kernel principal component analysis** — (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non linear mapping.ExampleThe two … Wikipedia

**Principal components analysis** — Principal component analysis (PCA) is a vector space transform often used to reduce multidimensional data sets to lower dimensions for analysis. Depending on the field of application, it is also named the discrete Karhunen Loève transform (KLT),… … Wikipedia

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