# Difference between revisions of "Principal component analysis"

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(Created page with "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...") |
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Operatively, this entails: | Operatively, this entails: | ||

; Computing a cross-correlation matrix | ; 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 | + | : 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 | ; Computing the eigenvalues, eigenvolumes and eigencomponents | ||

; Using the eigencomponents to create a classification. | ; Using the eigencomponents to create a classification. |

## Revision as of 10:34, 18 April 2016

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.

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:

- 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.