Locality-Constraint Discriminative Nonnegative Representation for Pattern Classification

Ziqi Li, Hongcheng Song, Hefeng Yin, Yonghong Zhang () and Guangyong Zhang
Additional contact information
Ziqi Li: School of Automation, Wuxi University, Wuxi 214105, China
Hongcheng Song: School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China
Hefeng Yin: School of Automation, Wuxi University, Wuxi 214105, China
Yonghong Zhang: School of Automation, Wuxi University, Wuxi 214105, China
Guangyong Zhang: School of Science, Wuxi University, Wuxi 214105, China

Mathematics, 2023, vol. 12, issue 1, 1-16

Abstract: Representation-based classification methods (RBCM) have recently garnered notable attention in the field of pattern classification. Diverging from conventional methods reliant on ℓ 1 or ℓ 2 -norms, the nonnegative representation-based classifier (NRC) enforces a nonnegative constraint on the representation vector, thus enhancing the representation capabilities of positively correlated samples. While NRC has achieved substantial success, it falls short in fully harnessing the discriminative information associated with the training samples and neglects the locality constraint inherent in the sample relationships, thereby limiting its classification power. In response to these limitations, we introduce the locality-constraint discriminative nonnegative representation (LDNR) method. LDNR extends the NRC framework through the incorporation of a competitive representation term. Recognizing the pivotal role played by the estimated samples in the classification process, we include estimated samples that involve discriminative information in this term, establishing a robust connection between representation and classification. Additionally, we assign distinct local weights to different estimated samples, augmenting the representation capacity of homogeneous samples and, ultimately, elevating the performance of the classification model. To validate the effectiveness of LDNR, extensive comparative experiments are conducted on various pattern classification datasets. The findings demonstrate the competitiveness of our proposed method.

Keywords: pattern classification; nonnegative representation; locality constraint; dicriminative information (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/1/52/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/1/52/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2023:i:1:p:52-:d:1306234

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().