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  2. Matrix norm - Wikipedia

    en.wikipedia.org/wiki/Matrix_norm

    The Frobenius norm is an extension of the Euclidean norm to and comes from the Frobenius inner product on the space of all matrices. The Frobenius norm is sub-multiplicative and is very useful for numerical linear algebra. The sub-multiplicativity of Frobenius norm can be proved using Cauchy–Schwarz inequality.

  3. Perron–Frobenius theorem - Wikipedia

    en.wikipedia.org/wiki/Perron–Frobenius_theorem

    Let = be an positive matrix: > for ,.Then the following statements hold. There is a positive real number r, called the Perron root or the Perron–Frobenius eigenvalue (also called the leading eigenvalue, principal eigenvalue or dominant eigenvalue), such that r is an eigenvalue of A and any other eigenvalue λ (possibly complex) in absolute value is strictly smaller than r, |λ| < r.

  4. Matrix regularization - Wikipedia

    en.wikipedia.org/wiki/Matrix_regularization

    One example is the squared Frobenius norm, which can be viewed as an -norm acting either entrywise, or on the singular values of the matrix: = ‖ ‖ = | | = ⁡ =. In the multivariate case the effect of regularizing with the Frobenius norm is the same as the vector case; very complex models will have larger norms, and, thus, will be penalized ...

  5. Low-rank approximation - Wikipedia

    en.wikipedia.org/wiki/Low-rank_approximation

    In mathematics, low-rank approximation refers to the process of approximating a given matrix by a matrix of lower rank. More precisely, it is a minimization problem, in which the cost function measures the fit between a given matrix (the data) and an approximating matrix (the optimization variable), subject to a constraint that the approximating matrix has reduced rank.

  6. Non-negative matrix factorization - Wikipedia

    en.wikipedia.org/wiki/Non-negative_matrix...

    In Learning the parts of objects by non-negative matrix factorization Lee and Seung [43] proposed NMF mainly for parts-based decomposition of images. It compares NMF to vector quantization and principal component analysis , and shows that although the three techniques may be written as factorizations, they implement different constraints and ...

  7. Jensen's inequality - Wikipedia

    en.wikipedia.org/wiki/Jensen's_inequality

    Jensen's inequality generalizes the statement that a secant line of a convex function lies above its graph. Visualizing convexity and Jensen's inequality In mathematics , Jensen's inequality , named after the Danish mathematician Johan Jensen , relates the value of a convex function of an integral to the integral of the convex function.

  8. Induced representation - Wikipedia

    en.wikipedia.org/wiki/Induced_representation

    In group theory, the induced representation is a representation of a group, G, which is constructed using a known representation of a subgroup H.Given a representation of H, the induced representation is, in a sense, the "most general" representation of G that extends the given one.

  9. Schatten norm - Wikipedia

    en.wikipedia.org/wiki/Schatten_norm

    The Schatten 2-norm is the Frobenius norm. ... The latter version of Hölder's inequality is proven in higher ... Theory of Quantum Information, 2.3 Norms of ...