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Positive and Negative Label-Driven Nonnegative Matrix Factorization.
- Source :
-
IEEE Transactions on Circuits & Systems for Video Technology . Jul2021, Vol. 31 Issue 7, p2698-2710. 13p. - Publication Year :
- 2021
-
Abstract
- Positive label is often used as the supervisory information in the learning scenario, which refers to the category that a sample is assigned to. However, another side information lying in the labels, which describes the categories that a sample is exclusive of, have been largely ignored. In this paper, we propose a nonnegative matrix factorization (NMF) based classification method leveraging both positive and negative label information, which is termed as positive and negative label-driven NMF (PNLD-NMF). The proposed scheme concurrently accomplishes data representation and classification in a joint manner. Owing to the complementary characteristics between positive and negative labels, we further design a new regularization framework to take advantage of these two label types. Extensive experiments on six image classification benchmark datasets show that the proposed scheme is able to consistently deliver better classification accuracy. [ABSTRACT FROM AUTHOR]
- Subjects :
- *MATRIX decomposition
*NONNEGATIVE matrices
*TASK analysis
Subjects
Details
- Language :
- English
- ISSN :
- 10518215
- Volume :
- 31
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Circuits & Systems for Video Technology
- Publication Type :
- Academic Journal
- Accession number :
- 151282997
- Full Text :
- https://doi.org/10.1109/TCSVT.2020.3027570