# Difference between revisions of "Principal component analysis"

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[[Category:PCA]] | [[Category:PCA]] | ||

− | In general, a Principal Component Analysis aims at analyzing a data set and discovering a set of coordinates that capture the most representative features of said data. | + | [[Category:Classification]] |

+ | In general, a Principal Component Analysis (PCA) aims at analyzing a data set and discovering a set of coordinates that capture the most representative features of said data. Often the term ''PCA classification'' is used, although PCA is not a classification method: classification itself is performed on the features extracted through PCA. | ||

In ''Dynamo'', the PCA is the process of finding a reduced set of "eigenvolumes" that allow to approximatively represent each particle in our data set as a combination of these eigenvolumes. Which this representation, a generic particle can be represented by the contributions of each "eigenvolume" to the particle, i.e., by a set of "eigencomponents", normally in a number no much higher than 20. | In ''Dynamo'', the PCA is the process of finding a reduced set of "eigenvolumes" that allow to approximatively represent each particle in our data set as a combination of these eigenvolumes. Which this representation, a generic particle can be represented by the contributions of each "eigenvolume" to the particle, i.e., by a set of "eigencomponents", normally in a number no much higher than 20. |

## Revision as of 09:39, 19 April 2016

In general, a Principal Component Analysis (PCA) aims at analyzing a data set and discovering a set of coordinates that capture the most representative features of said data. Often the term *PCA classification* is used, although PCA is not a classification method: classification itself is performed on the features extracted through PCA.

In *Dynamo*, the PCA is the process of finding a reduced set of "eigenvolumes" that allow to approximatively represent each particle in our data set as a combination of these eigenvolumes. Which this representation, a generic particle can be represented by the contributions of each "eigenvolume" to the particle, i.e., by a set of "eigencomponents", normally in a number no much higher than 20.

Once the particles are represent by small sets of scalars, they can be classified with standard methods like k-means.

Operatively, this entails:

- Selecting the input
- a data folder
- a table
- a mask
- Computing a cross-correlation matrix
- this is typically the most consuming part, as it involves to compare all particles in the data folder against all particles.
- Computing the eigenvalues, eigenvolumes and eigencomponents
- Using the eigencomponents to create a classification.