# Principal component analysis

# Operative steps

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.