Locality-Constraint Discriminative Nonnegative Representation for Pattern Classification

Abstract

Representation-based classification methods (RBCM) have recently garnered notable attention in the field of pattern classification. Diverging from conventional methods reliant on ℓ1 or ℓ2-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.