*2.2.2.3 Steps for PCA algorithm*

**Step 1: To obtain the dataset:** First, split the input dataset into two halves, X and Y, where X represents the training set and Y represents the validation set;

**Step 2: Putting information into a structure:** Now create a structure to represent dataset, and use the two-dimensional matrix of independent variable X as an example. Here, each row represents a data item and each column represents a feature. The dataset's dimensions are determined by the number of columns;


*Machine Learning Algorithms from Wireless Sensor Network's Perspective DOI: http://dx.doi.org/10.5772/intechopen.111417*


The PCA algorithm allows the minimal variance components to be dropped because they simply have the least amount of information and reduce dimensionality. This could reduce the amount of data being communicated between sensor nodes in WSN scenarios by obtaining a small pair of uncorrelated linear combined innovative readings. By permitting the selection of only significant principle components and eliminating other lower order inconsequential components from the model, it can also turn the problem of vast data into one of tiny data.
