Dimensionality Reduction Techniques – PCA, Kernel-PCA and LDA Using Python


In this article, we will be looking at some of the useful techniques on how to reduce dimensionality in our datasets. When we talk about dimensionality,  we are referring to the number of columns in our dataset assuming that we are working on a tidy and a clean dataset. When we have many columns in our dataset, for example, more than ten, then the data is considered high dimensional. If we are new to the dataset, then it becomes extremely difficult to find the patterns within that dataset due to the complexity that comes with the high dimensional datasets.