The rank of reduced dispersion matrices

Reduced Rank Approximations of Transition Matrices

Parameter expansion for estimation of reduced rank covariance matrices

1 ‘Parameter expanded’ and standard expectation maximisation algorithms are de2 scribed for reduced rank estimation of covariance matrices by restricted maximum 3 likelihood, fitting the leading principal components only. Convergence behaviour of 4 these algorithms is examined for several examples and contrasted to that of the aver5 age information algorithm, and implications for practical anal...

On higher rank numerical hulls of normal matrices

‎In this paper‎, ‎some algebraic and geometrical properties of the rank$-k$ numerical hulls of normal matrices are investigated‎. ‎A characterization of normal matrices whose rank$-1$ numerical hulls are equal to their numerical range is given‎. ‎Moreover‎, ‎using the extreme points of the numerical range‎, ‎the higher rank numerical hulls of matrices of the form $A_1 oplus i A_2$‎, ‎where $A_1...

On Reduced Rank Nonnegative Matrix Factorization for Symmetric Nonnegative Matrices

Let V ∈ R be a nonnegative matrix. The nonnegative matrix factorization (NNMF) problem consists of finding nonnegative matrix factors W ∈ R and H ∈ R such that V ≈ WH. Lee and Seung proposed two algorithms which find nonnegative W and H such that ‖V −WH‖F is minimized. After examining the case in which r = 1 about which a complete characterization of the solution is possible, we consider the ca...

Reduced Basis Methods: From Low-Rank Matrices to Low-Rank Tensors

Abstract. We propose a novel combination of the reduced basis method with low-rank tensor techniques for the efficient solution of parameter-dependent linear systems in the case of several parameters. This combination, called rbTensor, consists of three ingredients. First, the underlying parameter-dependent operator is approximated by an explicit affine representation in a low-rank tensor forma...