In this article, you’ll learn about the eigendecomposition of a matrix. One way to understand it is to consider it as a special change of basis. You’ll first learn about eigenvectors and eigenvalues and then you’ll see how they can be applied to Principal Component Analysis (PCA).
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Matrix decomposition, also called matrix factorization is the process of splitting a matrix into multiple pieces. In the context of data science, you can for instance use it to select parts of the data, aimed at reducing dimensionality without losing much information (as for instance in Principal Component Analysis, as you’ll later in this post).
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