Spectral embedding is a technique used in spectral clustering that transforms high-dimensional data into a lower-dimensional space by utilizing the eigenvalues and eigenvectors of a similarity matrix. This method aims to preserve the structure of the data, allowing for more efficient clustering and visualization. By focusing on the leading eigenvectors, spectral embedding effectively captures the intrinsic geometry of the data, facilitating the separation of different clusters.
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