Dimensionality Reduction

Principal component analysis (PCA)

19 Feb , 2015  

Why PCA?

A principal component analysis is a way to reduce dimensionality of a data set consisting of numeric vectors to a lower dimensionality. Then it is possible to visualize the data set in three or less dimensions. This is analogous to lowering down the Rank of the Matrix which means that we decompose the Matrix into lower order one such that there is no more linear  dependency of one feature , on the other features or combination of features. The algorithm

  1. From every matrix element of P we subtract the mean of every element located in the same column. This new matrix we name P’. Thas is called mean-correction.
  2. More…

, ,