Results 111 to 120 of about 341,424 (240)

Quaternion Nuclear Norms Over Frobenius Norms Minimization for Robust Matrix Completion

Journal of Scientific Computing
Recovering hidden structures from incomplete or noisy data remains a pervasive challenge across many fields, particularly where multi-dimensional data representation is essential. Quaternion matrices, with their ability to naturally model multi-dimensional data, offer a promising framework for this problem.
Yu Guo, Guoqing Chen, Tieyong Zeng, Qiyu Jin, Michael Kwok-Po Ng   +4 more
openaire   +2 more sources

As‐Rigid‐As‐Possible Regularization for Implicit Surfaces

Computer Graphics Forum, EarlyView.
Abstract Implicit surface representations have regained popularity because of their use in machine learning. A common component in optimization is regularization, penalizing the deviation of the surface from its original shape. The popular as‐rigid‐as‐possible (Arap) energy strikes a good compromise between realistic deformation behavior and efficient ...
T. Djuren, M. Worchel, U. Finnendahl, M. Alexa   +3 more
wiley   +1 more source

On Bending in the As‐Rigid‐As‐Possible Deformation Energy

Computer Graphics Forum, EarlyView.
Abstract The well‐established As‐Rigid‐As‐Possible (ARAP) energy has various forms. For surface deformation, commonly used energies contain an implicit bending penalty. We present a natural, continuous generalization that incorporates multiple ARAP versions with an implicit, user‐controllable bending penalty.
Ugo Finnendahl, Marc Alexa
wiley   +1 more source

Graph regularised sparse NMF factorisation for imagery de‐noising

IET Computer Vision, 2018
When utilising non‐negative matrix factorisation (NMF) to decompose a data matrix into the product of two low‐rank matrices with non‐negative entries, the noisy components of data may be introduced into the matrix.
Yixian Fang, Huaxiang Zhang, Yuwei Ren
doaj   +1 more source

ON PARLETTS MATRIX NORM INEQUALITY FOR THE CHOLESKY DECOMPOSITION

, 2015
We show that a certain matrix norm ratio studied by Parlett has a supremum that is O(root n) when the chosen norm is the Frobenius norm, while it is O(log n) for the 2-norm. This ratio arises in Parlett's analysis of the Cholesky decomposition of an n by
MASCARENHAS, WF, EDELMAN, A
core   +1 more source

Tangent Blow‐Ups for Processing Non‐Manifold Geometry

Computer Graphics Forum, EarlyView.
Abstract Many geometry processing pipelines implicitly assume their input data is a manifold, or is sampled from one, with a unique tangent plane at every point. Geometric data, however, routinely contains sharp features like edges, corners, self‐intersections, branching junctions, and other singularities, rendering standard methods ill‐defined at ...
Alice Petrov, Mohammad Sina Nabizadeh, Ana Dodik, Justin Solomon   +3 more
wiley   +1 more source

A Frobenius-Norm Approach to Block Matrices and Singular Value Estimates

AppliedMath
We introduce a Frobenius-norm-based class of block matrices in which the interaction between diagonal and off-diagonal blocks is measured directly through the preconditioned blocks Aii−1Aij.
Ksenija Doroslovački, Dragana Cvetković, Irena Prodanović   +2 more
doaj   +1 more source

Unconditional Variance Estimation Under Complex Surveys

International Statistical Review, EarlyView.
Summary The unconditional framework treats the samples and the variables of interest as random variables. This is particularly suitable with analytic inference, when modelling survey data. We show that variance estimation does not involve finite population corrections and joint‐inclusion probabilities, even with large sampling fractions and under ...
Yves G. Berger
wiley   +1 more source

Neutral-Type CGNNs With Multiple Delays: A Frobenius Norm Approach to Stability

IEEE Access
For neutral Cohen-Grossberg neural networks incorporating multiple time delays, we construct a tailored Lyapunov function to derive stability criteria independent of delay magnitudes.
Lin Wang, Binbin Gan, Hao Chen
doaj   +1 more source

Missing Values in Time Series: A Brief Review and a New Versatile Imputation Method

International Statistical Review, EarlyView.
Summary Missing data can significantly hamper standard time series analysis, yet they occur frequently in applications. In this paper, we briefly review some available methods for handling missing values and introduce the temporal Wasserstein imputation, a novel method for imputing missing data in time series.
Shuo‐Chieh Huang, Tengyuan Liang, Ruey S. Tsay   +2 more
wiley   +1 more source