Component analysis technology is used to identify uncorrelated variables within a large data set. Analysts primarily use this technique to extract information from complex data sets. Commonly known as principle component analysis, this technique finds wide use in face recognition, computer graphics, and image compression.
Analysts often face the challenge of identifying a variable when there are too many correlations in an observation. PCA technology makes it easier for analysts to eliminate some variables hence avoid multi-collinearity.
Component Analysis has been a success in statistics due to its amazing features. Firstly, the technique preserves as much variation as possible even when the dimensionality of data is reduced. The technique is also unsupervised, meaning group data may not necessarily be used when reducing data dimension. Most importantly, this technique provides a synchronized low-dimensional representation of data. This attribute makes it easier to visually identify variables from a large data set.