Principle Component Analysis (PCA)
PCA is a method for transforming features in a dataset by combining them into uncorrelated linear combinations.
These new features, or principal components, sequentially maximize the variance represented (i.e. the first principal component has the most variance, the second principal component has the second most, and so on).
As a result, PCA is useful for dimensionality reduction because you can set an arbitrary variance cutoff.
Principal Component Analysis (PCA) is a method for finding the common variance in a series of variables and isolating it in a series of new variables, called Principal Components or Factors. Unlike the raw variables, these Principal Components are mutually independent (uncorrelated); they are related to the original variables by a series of correlations called loadings; a principal component is defined by the raw variables on which it loads highly.
Suppose you have 5 different groups of songs telling you about the genre of that songs. Means you have 5 different groups each representing a particular type of genre. For any new song to decide in which group you should put it but lets this song is of mix genre and you are unable to tag it to a particular group. Principal component analysis will help you here. PCA let’s you find out the main component out of several on which a particular outcome is dependent upon. it will help you identify one major genre so that you can place your song in that particular group neglecting other dependencies.