Eigenvalue
In linear algebra, an eigenvalue of a square matrix is a scalar that represents how a linear transformation changes the direction of the corresponding eigenvector. In data science, eigenvalues are used in principal component analysis (PCA) to reduce the dimensionality of a dataset by identifying the most important features. The eigenvalues represent the amount of variance explained by each principal component, and the eigenvectors represent the direction of the principal components. Eigenvalues are also used in machine learning algorithms such as singular value decomposition (SVD) and spectral clustering.
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