Eigenvector
In linear algebra, an eigenvector of a square matrix is a non-zero vector that, when the matrix is multiplied by it, results in a scalar multiple of the same vector, i.e., the vector changes only in scale. In data science, eigenvectors are used in principal component analysis (PCA) to reduce the dimensionality of a dataset while retaining as much of the original variation as possible. Eigenvectors are also used in machine learning algorithms such as clustering and classification. They are particularly useful in image and signal processing, where they can be used to extract features from the data.
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