Discriminative semi-supervised non-negative matrix factorization for data clustering

Xing, Zhiwei; Wen, Meng; Peng, Jigen; Feng, Jinqian

doi:10.1016/j.engappai.2021.104289

Cited by 20 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…">GSSNMF 34 (semisupervised) employs the label information to construct two complementary regularization terms to guide matrix factorization. DSSNMF 33 (semisupervised) utilizes the label information as a discriminatory regularization term to ensure the data with different labels are not classified into the same group.…”

Section: Methodsmentioning

confidence: 99%

“…8. DSSNMF 33 (semisupervised) utilizes the label information as a discriminatory regularization term to ensure the data with different labels are not classified into the same group.…”

Section: Compared Methodsmentioning

confidence: 99%

“…There are a large number of SSNMF methods that have been proposed. [31][32][33][34][35]37,38 This paper first reviews the SSNMF with hard constraints named constrained nonnegative matrix factorization (CNMF). 37 Suppose that samples denote as…”

Section: Semisupervised Nmfmentioning

confidence: 99%

“…To overcome this problem, Mygdalis et al 32 proposed a semisupervised subclass support vector (S3SVDD) method using the local neighborhood information between labeled and unlabeled data. Xing et al 33 proposed a discriminative semisupervised NMF (DSSNMF) method using partial labeling information as discriminative constraints to increase the distinguishability of data points. Different from the previous methods, Jia et al 34 proposed a new SSNMF method, which utilizes the label information of data to construct similar and dissimilar complementary regularization terms to guide the procedure of matrix factorization.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

SDNMF: Semisupervised discriminative nonnegative matrix factorization for feature learning

Lai

Wang

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

As one of the most effective feature learning methods, Nonnegative Matrix Factorization (NMF) has been widely used in many scientific fields, such as computer vision, data mining, and bioinformatics. However, NMF is an unsupervised method that cannot fully utilize the label information of data. Thus, its performance is limited in some recognition and classification problems. To remedy this shortcoming, this paper proposes a Semisupervised Discriminative NMF (SDNMF) method. First, we design a Soft‐Labeled NMF (SLNMF) model by introducing a soft‐label matrix‐based regression term into the original NMF, so that the relationship between the soft‐label matrix and low‐dimensional features can be constructed to improve the discriminative ability of low‐dimensional features. Second, to effectively estimate the soft‐label matrix, a Label Propagation (LP) model is adopted to fully explore the spatial distribution relationship between the labeled and unlabeled samples. Third, an Adaptive Graph Learning (AGL) model is proposed to exploit the geometric relationship of samples well, which could enhance the performance of LP. Finally, the above three models (i.e., SLNMF, LP, and AGL) are integrated into a unified framework for effective feature learning, which can not only effectively explore the structural relationship matrix between data, but also predict the labels for unknown samples. Moreover, an iterative optimization algorithm is presented to solve our objective function. The convergence and computational complexity analysis of the proposed SDNMF method are also provided. Extensive experiments are conducted on several standard data sets. Compared with related methods, the experimental results verify that the proposed SDNMF method achieves better performance.

show abstract

Section: Methodsmentioning

confidence: 99%

“…8. DSSNMF 33 (semisupervised) utilizes the label information as a discriminatory regularization term to ensure the data with different labels are not classified into the same group.…”

Section: Compared Methodsmentioning

confidence: 99%

Section: Semisupervised Nmfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

SDNMF: Semisupervised discriminative nonnegative matrix factorization for feature learning

Lai

Wang

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…During the decomposing procedure one can observe that the nonnegative constraints are enforced into the decomposed factors, NMF often can result in a parts-based data representation [2]. Usually, the advantages of the parts-based representation in NMF have been proved in different fields, and they also have significant improvements for the performance of NMF-based methods in many real-world applications [12,13]. Although the standard NMF model has obtained much attentions in recent decades [14], it still has some critical defects.…”

Section: Introductionmentioning

confidence: 99%

Multiple Graph Adaptive Regularized Semi-Supervised Nonnegative Matrix Factorization with Sparse Constraint for Data Representation

Zhang

Wang

et al. 2022

Processes

View full text Add to dashboard Cite

Multiple graph and semi-supervision techniques have been successfully introduced into the nonnegative matrix factorization (NMF) model for taking full advantage of the manifold structure and priori information of data to capture excellent low-dimensional data representation. However, the existing methods do not consider the sparse constraint, which can enhance the local learning ability and improve the performance in practical applications. To overcome this limitation, a novel NMF-based data representation method, namely, the multiple graph adaptive regularized semi-supervised nonnegative matrix factorization with sparse constraint (MSNMFSC) is developed in this paper for obtaining the sparse and discriminative data representation and increasing the quality of decomposition of NMF. Particularly, based on the standard NMF, the proposed MSNMFSC method combines the multiple graph adaptive regularization, the limited supervised information and the sparse constraint together to learn the more discriminative parts-based data representation. Moreover, the convergence analysis of MSNMFSC is studied. Experiments are conducted on several practical image datasets in clustering tasks, and the clustering results have shown that MSNMFSC achieves better performance than several most related NMF-based methods.

show abstract

Node Importance-Based Semi-supervised Nonnegative Matrix Factorization for Image Clustering

Wu,

Yang

2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

Discriminative semi-supervised non-negative matrix factorization for data clustering

Cited by 20 publications

References 23 publications

SDNMF: Semisupervised discriminative nonnegative matrix factorization for feature learning

SDNMF: Semisupervised discriminative nonnegative matrix factorization for feature learning

Multiple Graph Adaptive Regularized Semi-Supervised Nonnegative Matrix Factorization with Sparse Constraint for Data Representation

Node Importance-Based Semi-supervised Nonnegative Matrix Factorization for Image Clustering

Contact Info

Product

Resources

About