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Dual semi-supervised convex nonnegative matrix factorization for data representation
- Source :
- Information Sciences. 585:571-593
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- Semi-supervised nonnegative matrix factorization (NMF) has received considerable attention in machine learning and data mining. A new semi-supervised NMF method, called dual semi-supervised convex nonnegative matrix factorization (DCNMF), is proposed in this paper for fully using the limited label information. Specifically, DCNMF simultaneously incorporates the pointwise and pairwise constraints of labeled samples as dual supervisory information into convex NMF, which results in a better low-dimensional data representation. Moreover, DCNMF imposes the nonnegative constraint only on the coefficient matrix but not on the base matrix. Consequently, DCNMF can process mixed-sign data, and hence enlarge the range of applications. We derive an efficient alternating iterative algorithm for DCNMF to solve the optimization, and analyze the proposed DCNMF method in terms of the convergence and computational complexity. We also discuss the relationships between DCNMF and several typical NMF based methods. Experimental results illustrate that DCNMF outperforms the related state-of-the-art NMF methods on nonnegative and mixed-sign datasets for clustering applications. This work is supported in part by the National Nature Science Foundation of China (No. U1701266, 91648208, 61976175), and Guangdong Intellectual Property Big Data Key Laboratory (No. 2018B030322016).
- Subjects :
- Data Representation
Pointwise
Information Systems and Management
Computational complexity theory
Computer science
Iterative method
Semi-Supervised Learning
External Data Representation
Computer Science Applications
Theoretical Computer Science
Non-negative matrix factorization
Matrix (mathematics)
ComputingMethodologies_PATTERNRECOGNITION
Artificial Intelligence
Control and Systems Engineering
Electrical and electronic engineering [Engineering]
Cluster analysis
Coefficient matrix
Algorithm
Software
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 585
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi.dedup.....63b1ad4ddcbefc54699d5b4371699099