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Spectral Regularization

Spectral regularization controls how much a weight matrix can stretch inputs by constraining its largest singular value (spectral norm).

#spectral regularization#spectral norm#power iteration+11
∑MathIntermediate

Low-Rank Approximation

Low-rank approximation replaces a big matrix with one that has far fewer degrees of freedom while preserving most of its action.

#low-rank approximation
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#nuclear norm
#eckart-young theorem
#svd
+12
∑MathIntermediate

Matrix Norms & Condition Numbers

Matrix norms measure the size of a matrix in different but related ways, with Frobenius treating entries like a big vector, spectral measuring the strongest stretch, and nuclear summing all singular values.

#matrix norm#spectral norm#frobenius norm+12