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  2. Matrix factorization (algebra) - Wikipedia

    en.wikipedia.org/wiki/Matrix_factorization_(algebra)

    For a commutative ring and an element , a matrix factorization of is a pair of n-by-n matrices , such that =. This can be encoded more generally as a /-graded ...

  3. Matrix completion - Wikipedia

    en.wikipedia.org/wiki/Matrix_completion

    Matrix completion of a partially revealed 5 by 5 matrix with rank-1. Left: observed incomplete matrix; Right: matrix completion result. Matrix completion is the task of filling in the missing entries of a partially observed matrix, which is equivalent to performing data imputation in statistics. A wide range of datasets are naturally organized ...

  4. Matrix decomposition - Wikipedia

    en.wikipedia.org/wiki/Matrix_decomposition

    Decomposition: = where C is an m-by-r full column rank matrix and F is an r-by-n full row rank matrix; Comment: The rank factorization can be used to compute the Moore–Penrose pseudoinverse of A, [2] which one can apply to obtain all solutions of the linear system =.

  5. Matrix factorization (recommender systems) - Wikipedia

    en.wikipedia.org/wiki/Matrix_factorization...

    Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. [ 1 ]

  6. Matrix factorization of a polynomial - Wikipedia

    en.wikipedia.org/wiki/Matrix_factorization_of_a...

    In mathematics, a matrix factorization of a polynomial is a technique for factoring irreducible polynomials with matrices. David Eisenbud proved that every multivariate real-valued polynomial p without linear terms can be written as AB = pI , where A and B are square matrices and I is the identity matrix . [ 1 ]

  7. Non-negative matrix factorization - Wikipedia

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

    Non-negative matrix factorization (NMF or NNMF), also non-negative matrix approximation [1] [2] is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into (usually) two matrices W and H, with the property that all three matrices have no negative elements. This non-negativity makes the resulting ...

  8. Numerical linear algebra - Wikipedia

    en.wikipedia.org/wiki/Numerical_linear_algebra

    The QR factorization of a matrix is a matrix and a matrix so that A = QR, where Q is orthogonal and R is upper triangular. [1]: 50 [4]: 223 The two main algorithms for computing QR factorizations are the Gram–Schmidt process and the Householder transformation.

  9. Unique factorization domain - Wikipedia

    en.wikipedia.org/wiki/Unique_factorization_domain

    The question of when this happens is rather subtle: for example, for the localization of k[x, y, z]/(x 2 + y 3 + z 5) at the prime ideal (x, y, z), both the local ring and its completion are UFDs, but in the apparently similar example of the localization of k[x, y, z]/(x 2 + y 3 + z 7) at the prime ideal (x, y, z) the local ring is a UFD but ...