Factor analysis is used to find hidden factors that predict the values of observed variables within a set of data. It is mathematically similar to principal component analysis to the extent that some commentators and software packages regard the two as synonymous. The two algorithms often but by no means always produce very similar results for the same set of data. However, the aims of the two algorithms are different in that PCA simply aims to describe the observed data with a reduced number of dimensions, while factor analysis attempts to explain the relationships between the variables. Where the aim is feature discovery and especially trying to understand the relationships between variables rather than merely model them, then, factor analysis should be preferred to PCA.
- alias
- subtype
- has functional building block
- FBB_Feature discovery FBB_Dimensionality reduction
- has input data type
- IDT_Vector of quantitative variables
- has internal model
- INM_Function
- has output data type
- has learning style
- LST_Unsupervised
- has parametricity
- PRM_Nonparametric
- has relevance
- REL_Relevant
- uses
- sometimes supports
- mathematically similar to
- ALG_Principal component analysis