Generalised rank-constrained approximations of Hilbert-Schmidt operators on separable Hilbert spaces and applications
G Carere, HC Lie - arXiv preprint arXiv:2408.05104, 2024 - arxiv.org
In this work we solve, for given bounded operators $ B, C $ and Hilbert--Schmidt operator $
M $ acting on potentially infinite-dimensional separable Hilbert spaces, the reduced rank …
M $ acting on potentially infinite-dimensional separable Hilbert spaces, the reduced rank …
A Regularized Alternating Least‐Squares Method for Minimizing a Sum of Squared Euclidean Norms with Rank Constraint
P Soto-Quiros - Journal of Applied Mathematics, 2022 - Wiley Online Library
Minimizing a sum of Euclidean norms (MSEN) is a classic minimization problem widely used
in several applications, including the determination of single and multifacility locations. The …
in several applications, including the determination of single and multifacility locations. The …
Improvement in accuracy for dimensionality reduction and reconstruction of noisy signals. Part II: the case of signal samples
P Soto-Quiros, A Torokhti - Signal Processing, 2019 - Elsevier
In this paper, a novel interpretation of the problem of dimensionality reduction and
reconstruction of random signals is studied. The problem and its solution target highly noisy …
reconstruction of random signals is studied. The problem and its solution target highly noisy …
Least squares solutions to the rank-constrained matrix approximation problem in the Frobenius norm
H Wang - Calcolo, 2019 - Springer
In this paper, we discuss the following rank-constrained matrix approximation problem in the
Frobenius norm: ‖ C-AX ‖=\min‖ C-AX‖= min subject to rk\left (C_1-A_1 X\right)= b rk C …
Frobenius norm: ‖ C-AX ‖=\min‖ C-AX‖= min subject to rk\left (C_1-A_1 X\right)= b rk C …