Principle Component Analysis (PCA) is the most common form of dimensionality reduction that we can apply to features. Consider the example of a dataset consisting of two features and we would like to convert this two-dimensional data into one dimension. A natural approach would be to draw a line of the closest fit and project each data point onto this line, as shown in the following graph:
PCA attempts to find a surface to project the data by minimizing the distance between the data points and the line we are attempting to project this data to. For the more general case where we have n dimensions and we want to reduce this space to k-dimensions, we find k vectors u(1),u(2), ..., u(k) onto which to project the data so as to minimize the projection error. That is we are trying to find a k-dimensional surface to project the data.
This looks superficially like linear regression however it is different in several important ways. With linear regression we are trying...
