Principal Component Analysis: A brief mathematical introduction

Introduction Many times the datasets we have available contain a high number of variables and what we are looking for is to be able to explain the relationship between the variables. We can reduce the dimensionality of the dataset by obtaining a smaller number of variables that explain as much information as possible from the original data, and this is where Principal Component Analysis (PCA) comes into play. PCA is a mathematical method that belongs to the unsupervised learning methods (we do not have a target variable with labels) and one of its functions is to extract useful information from … Continue reading Principal Component Analysis: A brief mathematical introduction

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