Dimension Reduction techniques are one of the most useful methods in unsupervised learning of high dimensional datasets. In this post, we will learn how to use R to perform 6 most commonly used dimensionality reduction techniques, PCA: Principal Component Analysis SVD: Singular Value Decomposition ICA: Independent Component Analysis NMF: Non-negative Matrix Factorization tSNE UMAP We… Continue reading 6 Dimensionality Reduction Techniques in R (with Examples)
Tag: Dimensionality Reduction
Singular Value Decomposition (SVD) in Python
Matrix decomposition by Singular Value Decomposition (SVD) is one of the widely used methods for dimensionality reduction. For example, Principal Component Analysis often uses SVD under the hood to compute principal components. In this post, we will work through an example of doing SVD in Python. We will use gapminder data in wide form to… Continue reading Singular Value Decomposition (SVD) in Python