Principal component analysis
Revision as of 10:14, 18 April 2016 by Daniel Castaño (talk | contribs) (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...")
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.
