The term "eigengap" refers to the difference between two eigenvalues of a matrix, typically in the context of eigenvalue problems related to graph theory, machine learning, or numerical linear algebra. In many applications, particularly those dealing with spectral clustering, dimensionality reduction, and similar techniques, the eigengap can be a crucial indicator of how distinct the clusters or subspaces within the data are.
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