Results 61 to 70 of about 116,308 (181)

Graph Regularized Hierarchical Diffusion Process With Relevance Feedback for Medical Image Retrieval

IEEE Access, 2021
Befitting from the interpretability and the capacity in capturing the underlying manifold structure, diffusion process (DP) has attracted increasing attention in the field of image retrieval.
Liming Xu, Xiaopeng Yao, Lisha Zhong, Jianbo Lei, Zhiwei Huang   +4 more
doaj   +1 more source

Vacuum Polarization and Chiral Lattice Fermions

, 1995
The vacuum polarization due to chiral fermions on a 4--dimensional Euclidean lattice is calculated according to the overlap prescription. The fermions are coupled to weak and slowly varying background gauge and Higgs fields, and the polarization tensor ...
't Hooft, Alonso, Aoki, Aoki, Aoki, Aoki, Borelli, Chandrasekharan, Creutz, Creutz, Dugan, Frolov, Furman, Golterman, Golterman, Golterman, Itzykson, J Strathdee, Jansen, Jansen, Kaplan, Kaplan, Karsten, Karsten, Kawano, Korthals-Altes, Maiani, Montvay, Montvay, Narayanan, Narayanan, Narayanan, Narayanan, Narayanan, Narayanan, Narayanan, Nielsen, Nielsen, Nielsen, Randjbar-Daemi, Randjbar-Daemi, Randjbar-Daemi, Ross, S Randjbar-Daemi, Shamir, Shamir, Shamir, Slavnov, Slavnov, Smit, Smit, Swift, Yang   +52 more
core   +1 more source

Adaptive Kernel Graph Nonnegative Matrix Factorization

Information, 2023
Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized ...
Rui-Yu Li, Yu Guo, Bin Zhang
doaj   +1 more source

Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models [PDF]

, 2010
A challenging problem in estimating high-dimensional graphical models is to choose the regularization parameter in a data-dependent way. The standard techniques include $K$-fold cross-validation ($K$-CV), Akaike information criterion (AIC), and Bayesian ...
Liu, Han, Roeder, Kathryn, Wasserman, Larry   +2 more
core   +1 more source

Deep Graph Laplacian Regularization for Robust Denoising of Real Images

, 2019
Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitivity to training data and the complex nature of real image noise.
Cheung, Gene, Pang, Jiahao, Sun, Wenxiu, Zeng, Jin   +3 more
core   +1 more source

Regular Graphs are Antimagic

The Electronic Journal of Combinatorics, 2015
An undirected simple graph $G=(V,E)$ is called antimagic if there exists an injective function $f:E\rightarrow\{1,\dots,|E|\}$ such that $\sum_{e\in E(u)} f(e)\neq\sum_{e\in E(v)} f(e)$ for any pair of different nodes $u,v\in V$. In this note we prove — with a slight modification of an argument of Cranston et al. — that $k$-regular graphs are antimagic
Bérczi, Kristóf, Bernáth, Attila, Vizer, Máté   +2 more
openaire   +3 more sources

Joint Nonnegative Matrix Factorization Based on Sparse and Graph Laplacian Regularization for Clustering and Co-Differential Expression Genes Analysis

Complexity, 2020
The explosion of multiomics data poses new challenges to existing data mining methods. Joint analysis of multiomics data can make the best of the complementary information that is provided by different types of data.
Ling-Yun Dai, Rong Zhu, Juan Wang
doaj   +1 more source

Hyperspectral image denoising and destriping based on sparse representation, graph Laplacian regularization and stripe low-rank property

EURASIP Journal on Advances in Signal Processing, 2022
During the acquisition of a hyperspectral image (HSI), it is easily corrupted by many kinds of noises, which limits the subsequent applications. For decades, numerous HSI denoising methods have been proposed.
Zhi Zhang, Fang Yang
doaj   +1 more source

Cogrowth of regular graphs [PDF]

Proceedings of the American Mathematical Society, 1992
Let G \mathcal {G} be a d d -regular graph and T T the covering tree of G \mathcal {G} . We define a cogrowth constant of G \mathcal {G} in T T and express it in terms of the first eigenvalue of the Laplacian on
openaire   +2 more sources

Deep Domain Adaptation Based on Adversarial Network With Graph Regularization

IEEE Access, 2020
Although most transfer learning methods can reduce the difference of the feature distributions between the source and target domains effectively, some classes in the two domains may still be misaligned after domain adaptation, especially for the classes ...
Xu Jia, Na Ma, Fuming Sun
doaj   +1 more source