Singular Value Decomposition: Revision history

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    1 December 2024

    • curprev 16:1916:19, 1 December 2024‎ Dendrogram talk contribs‎ 2,936 bytes +2,936‎ 새 문서: '''Singular Value Decomposition (SVD)''' is a mathematical technique used to decompose a matrix into three component matrices. It is widely used in data analysis, dimensionality reduction, machine learning, and signal processing. ==Definition== SVD decomposes a matrix \( A \) into three matrices: *'''U:''' An orthogonal matrix containing the left singular vectors. *'''Σ (Sigma):''' A diagonal matrix with singular values sorted in descending order. *'''V^T:''' An orthogonal matr... Tag: Visual edit
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