Principal Component and Factor Analysis

Principal Component Analysis (PCA) and Factor Analysis are two methods that can help reveal simpler patterns within a complex set of variables. In particular, these methods seek to discover if the observed variables can be explained largely or entirely in terms of a much smaller number of variables called factors.

In mineral exploration, the most common application of these multivariate analysis methods is to characterize and map inter-relationships within high volume surface geochemistry data sets. Data volumes are a growing problem as geochemists seek to extract more information and knowledge from data sets with up to 50 or more variables.

The desire to simplify processing and analysis of large data sets is renewing interest in PCA and Factor Analysis algorithms and presentation. Requests from major exploration groups for these capabilities led to Geosoft developing PCA and Varimax Fator Analysis.

For more information on Principal Component and Factor Analysis, see Chapter 3 - Exploration Geochemistry System (Geochemistry™) Tutorial and User Guide.