SVD
Singular Value Decomposition (SVD) is a matrix factorization technique that decomposes a matrix into three matrices, U, Σ, and V, where U and V are orthogonal matrices and Σ is a diagonal matrix. SVD is widely used in data science for dimensionality reduction, noise reduction, and data compression. It is also used in recommender systems, image processing, and natural language processing. In SVD, the diagonal elements of Σ represent the singular values of the original matrix, which can be used to determine the importance of each feature in the data. The first few singular values can be used to approximate the original matrix, which can be useful for reducing the dimensionality of the data while preserving most of the information.
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