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16 papers · ranked by Valyu relevance

01
PPubMed21 Oct 2013
Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization Limited-Memory Fast Gradient Descent Method
Naiyang Guan, Lei Wei, Zhigang Luo, Dacheng Tao + 1 more
Graph regularized nonnegative matrix factorization (GNMF) decomposes a nonnegative data matrix to the product of two lower-rank nonnegative factor matrices, i.e., and ( ) and aims to preserve the local geometric structure of the dataset by minimizing squared Euclidean distance or Kullback-Leibler (KL) divergence…
97relevance
02
PPubMed6 Sep 2024
Microbe–disease associations prediction by graph regularized non‐negative matrix factorization with L 2 , 1 norm regularization terms Chen et al.
Ziwei Chen, Liangzhe Zhang, Jingyi Li, Hang Chen
Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and expensive. In this…
97relevance
03
PPubMed19 Aug 2025
Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation
Ling Zhong, Haiyan Gao, Friedhelm Schwenker
Clustering algorithms based on non-negative matrix factorization (NMF) have garnered significant attention in data mining due to their strong interpretability and computational simplicity. However, traditional NMF often struggles to effectively capture and preserve topological structure information between data during…
96relevance
04
PPubMed22 Oct 2019
Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data
Na Yu, Ying-Lian Gao, Jin-Xing Liu, Juan Wang + 1 more
Background As one of the most popular data representation methods, non-negative matrix decomposition (NMF) has been widely concerned in the tasks of clustering and feature selection. However, most of the previously proposed NMF-based methods do not adequately explore the hidden geometrical structure in the data. At the…
94relevance
05
PPubMed28 Jun 2024
Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition
Charles Broadbent, Tianci Song, Rui Kuang
We used three methods based on the factorizations shown in [btae245-F1] as well as NSFH as baseline comparisons, focusing on linear methods that are able to extract spatial components in spatial transcriptomics data so that they can be used for direct quantitative and visual comparison to GraphTucker. 1. Nonnegative…
91relevance
06
PPubMed10 Feb 2026
JGR-NMF: joint graph-regularized non-negative matrix factorization for spatial domain identification
Juan Liang, Jiuxi Huang, Chenxi Xi, Yun Wang + 2 more
The spatial transcriptomics technique provides an unprecedented perspective for analyzing the distribution patterns of cells within tissues and their functional tissue structures. To enhance the accuracy and robustness of spatial domain identification, we propose Joint Graph-Regularized Non-negative Matrix…
88relevance
07
PPubMed30 Mar 2017
Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation
Guoliang Yang, Zhengwei Hu
Aiming at the problem of gene expression profile's high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent low-rank representation (Lat-LRR). By introducing dual graph manifold regularized…
87relevance
08
PPubMed8 Apr 2019
Detecting drug communities and predicting comprehensive drug–drug interactions via balance regularized semi-nonnegative matrix factorization
Jian-Yu Shi, Kui-Tao Mao, Hui Yu, Siu-Ming Yiu
Background Because drug-drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approaches provide a much cheaper…
85relevance
09
PPubMed24 Dec 2019
Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization
Jiang Huang, Min Wu, Fan Lu, Le Ou-Yang + 1 more
Background Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are…
84relevance
10
PPubMed16 Jul 2021
Predicting lncRNA–Protein Interaction With Weighted Graph-Regularized Matrix Factorization
Xibo Sun, Leiming Cheng, Jinyang Liu, Cuinan Xie + 2 more
'Fu Li'] Long non-coding RNAs (lncRNAs) are widely concerned because of their close associations with many key biological activities. Though precise functions of most lncRNAs are unknown, research works show that lncRNAs usually exert biological function by interacting with the corresponding proteins. The experimental…
83relevance
11
PPubMed2 Nov 2012
A Framework for Regularized Non-Negative Matrix Factorization, with Application to the Analysis of Gene Expression Data Regularized Non-Negative Matrix Factorization
Leo Taslaman, Björn Nilsson, Magnus Rattray
Non-negative matrix factorization (NMF) condenses high-dimensional data into lower-dimensional models subject to the requirement that data can only be added, never subtracted. However, the NMF problem does not have a unique solution, creating a need for additional constraints (regularization constraints) to promote…
80relevance
12
PPubMed5 Dec 2017
Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks
Wenjun Wang, Xue Chen, Pengfei Jiao, Di Jin
Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of the complex networks. A variety of complex systems in real world should be abstractly represented as bipartite networks, in…
79relevance
13
PPubMed5 Dec 2024
Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding
Mingming Li, Xingjie Wang, Chunhua Li, Anping Zeng + 1 more
Text embedding plays a crucial role in natural language processing (NLP). Among various approaches, nonnegative matrix factorization (NMF) is an effective method for this purpose. However, the standard NMF approach, fundamentally based on the bag-of-words model, fails to utilize the contextual information of documents…
78relevance
14
PPubMed7 Oct 2010
Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)
Elina Tjioe, Michael W Berry, Ramin Homayouni
Background Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases…
75relevance
15
PPubMed3 Nov 2021
Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection
Shicheng Li, Shumin Lai, Yan Jiang, Wenle Wang + 1 more
Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the performance of…
72relevance
16
PPubMed2 Dec 2021
Efficient Proximal Gradient Algorithms for Joint Graphical Lasso
Jie Chen, Ryosuke Shimmura, Joe Suzuki, Mohamed Medhat Gaber
We consider learning as an undirected graphical model from sparse data. While several efficient algorithms have been proposed for graphical lasso (GL), the alternating direction method of multipliers (ADMM) is the main approach taken concerning joint graphical lasso (JGL). We propose proximal gradient procedures with…
61relevance

Results for graph regularized nonnegative matrix factorization

Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization Limited-Memory Fast Gradient Descent Method

Microbe–disease associations prediction by graph regularized non‐negative matrix factorization with L 2 , 1 norm regularization terms Chen et al.

Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation

Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data

Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition

JGR-NMF: joint graph-regularized non-negative matrix factorization for spatial domain identification

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

Detecting drug communities and predicting comprehensive drug–drug interactions via balance regularized semi-nonnegative matrix factorization

Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization

Predicting lncRNA–Protein Interaction With Weighted Graph-Regularized Matrix Factorization

A Framework for Regularized Non-Negative Matrix Factorization, with Application to the Analysis of Gene Expression Data Regularized Non-Negative Matrix Factorization

Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks

Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding

Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Efficient Proximal Gradient Algorithms for Joint Graphical Lasso

Limited-Memory Fast Gradient Descent Method for Graph Regularized Nonnegative Matrix Factorization Limited-Memory Fast Gradient Descent Method

Microbe–disease associations prediction by graph regularized non‐negative matrix factorization with L 2 , 1 norm regularization terms Chen et al.

Autoencoder-like Sparse Non-Negative Matrix Factorization with Structure Relationship Preservation

Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data

Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition

JGR-NMF: joint graph-regularized non-negative matrix factorization for spatial domain identification

Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation

Detecting drug communities and predicting comprehensive drug–drug interactions via balance regularized semi-nonnegative matrix factorization

Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization

Predicting lncRNA–Protein Interaction With Weighted Graph-Regularized Matrix Factorization

A Framework for Regularized Non-Negative Matrix Factorization, with Application to the Analysis of Gene Expression Data Regularized Non-Negative Matrix Factorization

Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks

Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding Nonnegative matrix factorization with Wasserstein metric-based regularization for enhanced text embedding

Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Efficient Proximal Gradient Algorithms for Joint Graphical Lasso