Eigenvalue problems are a cornerstone of modern applied mathematics, arising in diverse fields from quantum mechanics to structural engineering. At their heart, these problems seek scalar values and ...
But computing eigenvalues and eigenvectors directly is extremely difficult. However, it's possible to compute eigenvalues and eigenvectors indirectly using singular value decomposition (SVD).
Transforming a dataset into one with fewer columns is more complicated than it might seem, explains Dr. James McCaffrey of Microsoft Research in this full-code, step-by-step machine learning tutorial.
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