Results 1 to 10 of about 261,453 (169)
Axioms, 2021 In this survey article, we look into some recent results concerning summability matrices, both regular as well as those which are not regular (called semi-regular) and generated matrix ideals as the overall view of the inter relationship between the ...
Pratulananda Das
doaj +1 more source
Regularization for matrix completion [PDF]
2010 IEEE International Symposium on Information Theory, 2010 We consider the problem of reconstructing a low rank matrix from noisy observations of a subset of its entries. This task has applications in statistical learning, computer vision, and signal processing. In these contexts, "noise" generically refers to any contribution to the data that is not captured by the low-rank model.
Raghunandan H. Keshavan +1 more
openaire +2 more sources
Regularized Matrix Regression [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2013 SummaryModern technologies are producing a wealth of data with complex structures. For instance, in two-dimensional digital imaging, flow cytometry and electroencephalography, matrix-type covariates frequently arise when measurements are obtained for each combination of two underlying variables.
Zhou, Hua, Li, Lexin
openaire +3 more sources
Super Fuzzy Matrix of Inverse in kth Order
Ratio Mathematica, 2023 Unexpected event modelling is a affluent area of study in fuzzy matrix (FM) modelling. Every fuzzy matrix may be shown as a multidimensional cocept, but standard matrices cannot achieve this without the proper scale.
R. Deepa, Dr. P. Sundararajan
doaj +1 more source
Regularization in Matrix Relevance Learning [PDF]
IEEE Transactions on Neural Networks, 2010 In this paper, we present a regularization technique to extend recently proposed matrix learning schemes in learning vector quantization (LVQ). These learning algorithms extend the concept of adaptive distance measures in LVQ to the use of relevance matrices. In general, metric learning can display a tendency towards oversimplification in the course of
Petra Schneider +5 more
openaire +4 more sources
Learnable Graph-Regularization for Matrix Decomposition
ACM Transactions on Knowledge Discovery from Data, 2023 Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields, including computer vision, text mining, bioinformatics, and many others. They allow for embedding high-dimensional data into low-dimensional spaces, which mitigates the effects of noise and uncovers latent relations.
Penglong Zhai, Shihua Zhang
openaire +2 more sources
Adaptive and Implicit Regularization for Matrix Completion
SIAM Journal on Imaging Sciences, 2022 The explicit low-rank regularization, e.g., nuclear norm regularization, has been widely used in imaging sciences. However, it has been found that implicit regularization outperforms explicit ones in various image processing tasks. Another issue is that the fixed explicit regularization limits the applicability to broad images since different images ...
Zhemin Li +3 more
openaire +2 more sources
Implicit Regularization in Matrix Factorization [PDF]
2018 Information Theory and Applications Workshop (ITA), 2018 We study implicit regularization when optimizing an underdetermined quadratic objective over a matrix $X$ with gradient descent on a factorization of $X$. We conjecture and provide empirical and theoretical evidence that with small enough step sizes and initialization close enough to the origin, gradient descent on a full dimensional factorization ...
Suriya Gunasekar +4 more
openaire +3 more sources
Regularized Tapered Sample Covariance Matrix
IEEE Transactions on Signal Processing, 2022 Covariance matrix tapers have a long history in signal processing and related fields. Examples of applications include autoregressive models (promoting a banded structure) or beamforming (widening the spectral null width associated with an interferer).
Breloy, Arnaud, Ollila, Esa
openaire +4 more sources
Regularized LTI System Identification with Multiple Regularization Matrix [PDF]
IFAC-PapersOnLine, 2018 Abstract Regularization methods with regularization matrix in quadratic form have received increasing attention. For those methods, the design and tuning of the regularization matrix are two key issues that are closely related. For systems with complicated dynamics, it would be preferable that the designed regularization matrix can bring the hyper ...
Chen, Tianshi +5 more
openaire +1 more source