Component Analysis is a statistical procedure that utilises orthogonal transformation to convert given set data observations with correlated variables to linearly uncorrelated variables known as principal components.
This simplifies high dimensional data complexity retaining its patterns and trends. Simplification starts by transforming data into fewer dimensions, which serve as character summaries. High dimensional data is ubiquitous in biological fields, and it arises when multiple features are measured. An example of multiple features is many genes. This data is challenging that component analysis aims to reduce.
Reducing the number of data variables assuredly achieve accuracy and simplicity in carrying out the tests. Small data sets are easy to investigate, visualize and analyze faster without strenuous variable processes.
In summary, the concept of component analysis is explicitly simple-mitigate the variable data sets preserving original information for secure computation and analysis achieving the same intended results.
