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Graph Regularized Semi-Supervised Concept Factorization

Yu Qing Shi, Shi Qiang Du, Wei Lan Wang
2012 Advanced Engineering Forum  
A modified CF algorithm called Graph Regularized Semi-supervised Concept Factorization (GRSCF) is proposed for addressing the limitations of CF and Local Consistent Concept Factorization (LCCF), which  ...  Concept Factorization (CF) is a new matrix decomposition technique for data representation.  ...  Graph Regularized Semi-supervised Concept Factorization (GRSCF) Concept Factorization (CF).  ... 
doi:10.4028/www.scientific.net/aef.6-7.583 fatcat:urbbd3tpafgatlky2gsaysxeii

Group Sparsity and Graph Regularized Semi-Nonnegative Matrix Factorization with Discriminability for Data Representation

Peng Luo, Jinye Peng
2017 Entropy  
To settle these issues, in this paper, we propose a novel Semi-NMF algorithm, called Group sparsity and Graph regularized Semi-Nonnegative Matrix Factorization with Discriminability (GGSemi-NMFD) to overcome  ...  Semi-Nonnegative Matrix Factorization (Semi-NMF) , as a variant of NMF, inherits the merit of parts-based representation of NMF and possesses the ability to process mixed sign data, which has attracted  ...  We propose a novel algorithm, called Group sparsity and Graph regularized Semi-Nonnegative Matrix Factorization with Discriminability (GGSemi-NMFD), for data representation.  ... 
doi:10.3390/e19120627 fatcat:l5ipuakwuvh5fk2wwz6sasnknu

Graph Regularized Nonnegative Matrix Factorization for Hyperspectral Data Unmixing [article]

Roozbeh Rajabi, Mahdi Khodadadzadeh, Hassan Ghassemian
2011 arXiv   pre-print
This paper examines the applicability of a recently developed algorithm called graph regularized nonnegative matrix factorization (GNMF) for this aim.  ...  Simulated data based on the measured spectral signatures, is used for evaluating the proposed algorithm.  ...  Cai for his valuable help.  ... 
arXiv:1111.0885v1 fatcat:shosbxft5ngkfd2cjhp2umji7u

Graph-regularized multi-view semantic subspace learning

Jinye Peng, Peng Luo, Ziyu Guan, Jianping Fan
2017 International Journal of Machine Learning and Cybernetics  
MvSL learns a nonnegative latent space and tries to capture the semantic structure of data by a novel graph embedding framework, where an affinity graph characterizing intra-class compactness and a penalty  ...  In addition, we encourage each latent dimension to be associated with a subset of views via sparseness constraints.  ...  Unified Latent Factor method (SULF): SULF [14] is a semi-supervised multi-view nonnegative factorization method which factorizes partial label information as a constraint on l . • Graph regularized  ... 
doi:10.1007/s13042-017-0766-5 fatcat:jk47427hyjfarknicxuyom4a3y

Transductive Nonnegative Matrix Tri-Factorization

Xiao Teng, Long Lan, Xiang Zhang, Guohua Dong, Zhigang Luo
2020 IEEE Access  
INDEX TERMS Nonnegative matrix factorization, nonnegative matrix tri-factorization, transductive learning.  ...  Nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices.  ...  Lin for her assistance with the experiment.  ... 
doi:10.1109/access.2020.2989527 fatcat:ux7lny42gjag5ftaa2tvmf42ay

Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation

Lin Zhang, Zhonghua Liu, Lin Wang, Jiexin Pu
2019 IEEE Access  
In this work, we propose an adaptive graph regularization discriminant nonnegative matrix factorization (AGDNMF) for image clustering.  ...  INDEX TERMS Nonnegative matrix factorization, graph regularization, discriminative information, image clustering, data representation. 112756 This work is licensed under a Creative Commons Attribution  ...  CONCLUSION In this paper, we propose a novel semi-supervised adaptive graph regularization discriminant nonnegative matrix factorization (AGDNMF).  ... 
doi:10.1109/access.2019.2933877 fatcat:pbplvkzqojhl7anmdhy7nfa4ya

Robust semi-supervised nonnegative matrix factorization

Jing Wang, Feng Tian, Chang Hong Liu, Xiao Wang
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
In this paper, we propose a robust semi-supervised nonnegative matrix factorization (RSSNMF) approach which takes all factors into consideration.  ...  Nonnegative matrix factorization (NMF), which aims at finding parts-based representations of nonnegative data, has been widely applied to a wide range of applications such as data clustering, pattern recognition  ...  ROBUST SEMI-SUPERVISED NONNEGATIVE MATRIX FACTORIZATION (RSSNMF) A.  ... 
doi:10.1109/ijcnn.2015.7280422 dblp:conf/ijcnn/WangTLW15 fatcat:22ylmrtrkrc6pcymied3zylmqy

Safety Monitoring by A Graph-Regularized Semi-Supervised Nonnegative Matrix Factorization with Applications to A Vision- Based Marking Process

Song Fan, Qilong Jia, Wansheng Cheng
2020 IEEE Access  
The new safety monitoring method is developed with the aid of a new graph-regularized semi-supervised nonnegative matrix factorization (GSNMF) algorithm.  ...  Compared with the existing nonnegative matrix factorization (NMF)-like algorithms, GSNMF is developed in an all-new manner so that it not only can take advantage of images with known labels and images  ...  Next, we will propose a modified SNMF algorithm, which is referred to as graph-regularized semi-supervised nonnegative matrix factorization (GSNMF).  ... 
doi:10.1109/access.2020.3002802 fatcat:ndlvsxka25c3jjcqohjmi46lrq

Group Sparsity in Nonnegative Matrix Factorization [chapter]

Jingu Kim, Renato D. C. Monteiro, Haesun Park
2012 Proceedings of the 2012 SIAM International Conference on Data Mining  
In this paper, we develop group sparsity regularization methods for nonnegative matrix factorization (NMF).  ...  Efficient convex optimization methods for dealing with the mixed-norm term are presented along with computational comparisons between them.  ...  Figure 2 : 2 (a) Original latent factor images and recovered factor images with various regularization methods (b) Original coefficient matrix and recovered coefficient matrices with various regularization  ... 
doi:10.1137/1.9781611972825.73 dblp:conf/sdm/KimMP12 fatcat:i445z6hbhrdwjin7nv2zjcxiay

Multi-View Concept Learning for Data Representation

Ziyu Guan, Lijun Zhang, Jinye Peng, Jianping Fan
2015 IEEE Transactions on Knowledge and Data Engineering  
We propose Multi-view Concept Learning (MCL), a novel nonnegative latent representation learning algorithm for capturing conceptual factors from multi-view data.  ...  each latent factor to be associated with a subset of views via sparseness constraints.  ...  Semi-Supervision on V In order to let the learned latent space reflect the semantic relationships between items, we propose to regularize the consensus encoding matrix V by a graph embedding framework  ... 
doi:10.1109/tkde.2015.2448542 fatcat:r3rvslclhffiradwkp5wrxyivu

Transfer Nonnegative Matrix Factorization for Image Representation [chapter]

Tianchun Wang, TengQi Ye, Cathal Gurrin
2016 Lecture Notes in Computer Science  
Nonnegative Matrix Factorization (NMF) has received considerable attention due to its psychological and physiological interpretation of naturally occurring data whose representation may be partsbased in  ...  We accomplish this goal through TNMF -a novel semi-supervised transfer learning approach.  ...  Transfer Nonnegative Matrix Factorization In this section, we will present the Transfer Nonnegative Matrix Factorization (TNMF) algorithm for image representation, which extends HeNMF by taking into account  ... 
doi:10.1007/978-3-319-27674-8_1 fatcat:66u6x736ondonh4qy6gw66a2fu

A Deep Matrix Factorization Method for Learning Attribute Representations

George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, Bjorn W. Schuller
2017 IEEE Transactions on Pattern Analysis and Machine Intelligence  
Finally, we show that our models are able to learn low-dimensional representations that are better suited for clustering, but also classification, outperforming Semi-Non-negative Matrix Factorization,  ...  Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation.  ...  The responsibility lies with the authors.  ... 
doi:10.1109/tpami.2016.2554555 pmid:28113886 fatcat:mimkfywbnbdq5larrpdrfcyhka

Relational Multimanifold Coclustering

Ping Li, Jiajun Bu, Chun Chen, Zhanying He, Deng Cai
2013 IEEE Transactions on Cybernetics  
To achieve this, we develop a novel co-clustering algorithm called Relational Multi-manifold Co-clustering (RMC) based on symmetric nonnegative matrix tri-factorization, which decomposes the relational  ...  data matrix into three submatrices.  ...  . • NMF: Nonnegative matrix factorization [26] . • GNMF: Graph regularized nonnegative matrix factorization, which considers local geometrical structure by the sample graph regularization [6] . • DRCC  ... 
doi:10.1109/tsmcb.2012.2234108 pmid:23757578 fatcat:7ti3r5ogyzdpfbajjuav4eh2oa

Deep Multi-View Concept Learning

Cai Xu, Ziyu Guan, Wei Zhao, Yunfei Niu, Quan Wang, Zhiheng Wang
2018 Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence  
In this work we present a semi-supervised deep multi-view factorization method, named Deep Multi-view Concept Learning (DMCL).  ...  We develop a block coordinate descent algorithm for DMCL. Experiments conducted on image and document datasets show that DMCL performs well and outperforms baseline methods.  ...  The third term is the graph embedding criterion for regularizing V where tr(·) denotes matrix trace.  ... 
doi:10.24963/ijcai.2018/402 dblp:conf/ijcai/XuGZNWW18 fatcat:7gwo4xw42bhrhgm4r6vdodob2u

Graph Regularized Constrained Non-Negative Matrix Factorization with Lp Smoothness for Image Representation

Zhenqiu Shu, Zonghui Wen, Yunmeng Zhang, Congzhe You, Zhen Liu
2020 IEEE Access  
In this paper, we propose a graph regularized constrained nonnegative matrix factorization with L p Smoothing (GCNMFS) for image representation.  ...  Nonnegative matrix factorization-based image representation algorithms have been widely applied to deal with high-dimensional data in the past few years.  ...  In this work, we propose a novel method, called graph regularized constrained nonnegative matrix factorization with L p smoothing (GCNMFS), for data representation.  ... 
doi:10.1109/access.2020.3009261 fatcat:iotohnl3yjffxdaz34khgswuou
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