Singular Value Decomposition
Singular Value Decomposition (SVD) is a matrix factorization method that decomposes a matrix into three matrices, where the middle matrix contains the singular values of the original matrix. SVD is widely used in data science and machine learning for dimensionality reduction, data compression, and feature extraction. It is also used in recommender systems, image processing, and natural language processing. SVD can be used to find the most important features in a dataset, and to remove noise and redundancy from the data. It is a powerful tool for analyzing large datasets and for building predictive models. SVD is closely related to Principal Component Analysis (PCA), which is a linear transformation method that finds the directions of maximum variance in a dataset.
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