Hyper-graph regularized discriminative concept factorization for data representation
Sign Up to like & getrecommendations! Published in 2018 at "Soft Computing"
DOI: 10.1007/s00500-017-2636-1
Abstract: For the tasks of pattern analysis and recognition, nonnegative matrix factorization and concept factorization (CF) have attracted much attention due to its effective application to find the meaningful low-dimensional representation of data. However, they neglect… read more here.
Keywords: graph regularized; factorization; hyper graph; concept factorization ... See more keywords
Graph regularized nonnegative matrix tri-factorization for overlapping community detection
Sign Up to like & getrecommendations! Published in 2019 at "Physica A: Statistical Mechanics and its Applications"
DOI: 10.1016/j.physa.2018.09.093
Abstract: Abstract Non-negative Matrix Factorization technique has attracted many interests in overlapping community detection due to its performance and interpretability. However, when adapted to discover community structure the intrinsic geometric information of the network graph is… read more here.
Keywords: graph regularized; matrix; factorization; community detection ... See more keywords
Robust Graph Regularized NMF with Dissimilarity and Similarity Constraints for ScRNA-seq Data Clustering
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c01305
Abstract: The notable progress in single-cell RNA sequencing (ScRNA-seq) technology is beneficial to accurately discover the heterogeneity and diversity of cells. Clustering is an extremely important step during the ScRNA-seq data analysis. However, it cannot achieve… read more here.
Keywords: seq data; graph regularized; robust graph; scrna seq ... See more keywords
Enhanced IoT threat detection using Graph-Regularized neural networks optimized by Sea-Lion algorithm
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-10238-0
Abstract: The Internet of Things (IoT) has revolutionized business operations, but its interconnected nature introduces significant cyber security risks, including malware and software piracy that compromise sensitive data and organizational reputation. To address this challenge, we… read more here.
Keywords: detection using; graph regularized; iot threat; threat detection ... See more keywords
A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations
Sign Up to like & getrecommendations! Published in 2018 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btx545
Abstract: Motivation MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational… read more here.
Keywords: graph regularized; diseases mirnas; regularized non; disease associations ... See more keywords
Graph-Regularized Laplace Approximation for Detecting Small Infrared Target Against Complex Backgrounds
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2925563
Abstract: Against complex background containing the tiny target, high-performance infrared small target detection is always treated as a difficult task. Many low-rank recovery-based methods have shown great potential, but they may suffer from high false or… read more here.
Keywords: graph regularized; approximation detecting; background; rank ... See more keywords
Graph Regularized Variational Ladder Networks for Semi-Supervised Learning
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3038276
Abstract: To tackle the problem of semi-supervised learning (SSL), we propose a new autoencoder-based deep model. Ladder networks (LN) is an autoencoder-based method for representation learning which has been successfully applied on unsupervised learning and semi-supervised… read more here.
Keywords: graph regularized; regularized variational; ladder networks; semi supervised ... See more keywords
Total Variation Constrained Graph-Regularized Convex Non-Negative Matrix Factorization for Data Representation
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2020.3047576
Abstract: We propose a novel NMF algorithm, named Total Variation constrained Graph-regularized Convex Non-negative Matrix Factorization (TV-GCNMF), to incorporate total variation and graph Laplacian with convex NMF. In this model, the feature details of the data… read more here.
Keywords: graph regularized; variation constrained; total variation; constrained graph ... See more keywords
Distributed Graph Regularized Denoising via Constrained Chebyshev Polynomials
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2024.3421972
Abstract: The problem of denoising signals, defined over graph domains, using a regularization framework, is considered here. Using the $L^{2}$ norm, the optimum denoising operator involves a matrix inverse. Approximation of the operator via matrix polynomial… read more here.
Keywords: distributed graph; graph regularized; chebyshev polynomials; denoising via ... See more keywords
Graph Regularized Flow Attention Network for Video Animal Counting From Drones
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2021.3082297
Abstract: In this paper, we propose a large-scale video based animal counting dataset collected by drones (AnimalDrone) for agriculture and wildlife protection. The dataset consists of two subsets, i.e., PartA captured on site by drones and… read more here.
Keywords: video; graph regularized; regularized flow; animal counting ... See more keywords
Semi-Supervised Graph Regularized Deep NMF With Bi-Orthogonal Constraints for Data Representation
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2019.2939637
Abstract: Semi-supervised non-negative matrix factorization (NMF) exploits the strengths of NMF in effectively learning local information contained in data and is also able to achieve effective learning when only a small fraction of data is labeled.… read more here.
Keywords: representation; graph regularized; semi supervised; supervised graph ... See more keywords