Difference between revisions of "Principal component analysis"
Jump to navigation
Jump to search
Line 8: | Line 8: | ||
Operatively, this entails: | Operatively, this entails: | ||
+ | ; Selecting the input | ||
+ | : a data folder | ||
+ | : a table | ||
+ | : a mask | ||
; 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 12:26, 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:
- 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.