A Regularized Convex Nonnegative Matrix Factorization Model for signed network analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jia Wang ◽  
Rongjian Mu
Keyword(s):  
Network Analysis ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Factorization Model ◽  
Signed Network

scholarly journals A Multidimensional Nonnegative Matrix Factorization Model for Retweeting Behavior Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Mengmeng Wang ◽  
Wanli Zuo ◽  
Ying Wang
Keyword(s):  
Social Network ◽  
Matrix Factorization ◽  
Information Diffusion ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Behavior Prediction ◽  
Dynamic Social Networks ◽  
Factorization Model ◽  
Text Information ◽  
Interest Similarity

Today microblogging has increasingly become a means of information diffusion via user’s retweeting behavior. As a consequence, exploring on retweeting behavior is a better way to understand microblog’s transmissibility in the network. Hence, targeted at online microblogging, a directed social network, along with user-based features, this paper first built content-based features, which consisted of URL, hashtag, emotion difference, and interest similarity, based on time series of text information that user posts. And then we measure relationship-based factor in social network according to frequency of interactions and network structure which blend with temporal information. Finally, we utilize nonnegative matrix factorization to predict user’s retweeting behavior from user-based dimension and content-based dimension, respectively, by employing strength of social relationship to constrain objective function. The results suggest that our proposed method effectively increases retweeting behavior prediction accuracy and provides a new train of thought for retweeting behavior prediction in dynamic social networks.

Hyperspectral unmixing via deep matrix factorization

2017 ◽  
Vol 15 (06) ◽  
pp. 1750058 ◽  
Author(s):  
Lei Tong ◽  
Jing Yu ◽  
Chuangbai Xiao ◽  
Bin Qian
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
State Of The Art ◽  
Nonnegative Matrix ◽  
Real Data ◽  
Hyperspectral Unmixing ◽  
The Past ◽  
Factorization Model ◽  
Layer Structures ◽  
Hyperspectral Remote Sensing Image

Hyperspectral unmixing is one of the most important techniques in hyperspectral remote sensing image analysis. During the past decades, many models have been widely used in hyperspectral unmixing, such as nonnegative matrix factorization (NMF) model, sparse regression model, etc. Most recently, a new matrix factorization model, deep matrix, is proposed and shows good performance in face recognition area. In this paper, we introduce the deep matrix factorization (DMF) for hyperspectral unmixing. In this method, the DMF method is applied for hyperspectral unmixing. Compared with the traditional NMF-based unmixing methods, DMF could extract more information with multiple-layer structures. An optimization algorithm is also proposed for DMF with two designed processes. Results on both synthetic and real data have validated the effectiveness of this method, and shown that it has outperformed several state-of-the-art unmixing approaches.

scholarly journals GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

2019 ◽  
Author(s):  
Mei-Neng Wang ◽  
Zhu-Hong You ◽  
Li-Ping Li ◽  
Leon Wong ◽  
Zhan-Heng Chen ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Computational Prediction ◽  
Computational Method ◽  
Sequence Information ◽  
Labor Costs ◽  
Factorization Model ◽  
Limited Scale ◽  
Prediction Approach

AbstractLong non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been involved in various biological processes. Emerging evidence suggests that the interactions between lncRNAs and miRNAs play an important role in regulating of genes and the development of many diseases. Due to the limited scale of known lncRNA-miRNA interactions, and expensive time and labor costs for identifying them by biological experiments, more accurate and efficient lncRNA-miRNA interactions computational prediction approach urgently need to be developed. In this work, we proposed a novel computational method, GNMFLMI, to predict lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization. More specifically, the similarities both lncRNA and miRNA are calculated based on known interaction information and their sequence information. Then, the affinity graphs for lncRNAs and miRNAs are constructed using the p-nearest neighbors, respectively. Finally, a graph regularized nonnegative matrix factorization model is developed to accurately identify potential interactions between lncRNAs and miRNAs. To evaluate the performance of GNMFLMI, five-fold cross validation experiments are carried out. GNMFLMI achieves the AUC value of 0.9769 which outperforms the compared methods NMF and CNMF. In the case studies for lncRNA nonhsat159254.1 and miRNA hsa-mir-544a, 20 and 16 of the top-20 associations predicted by GNMFLMI are confirmed, respectively. Rigorous experimental results demonstrate that GNMFLMI can effectively predict novel lncRNA-miRNA interactions, which can provide guidance for relevant biomedical research.

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