We introduce a new classifier for small-sample image data based on a two-dimensional discriminative regression approach. For a test example, our method estimates a discriminative representation from training examples, which accounts for discriminativeness between classes and enables accurate derivation of categorical information. Unlike existing methods that vectored image data, the learning of the representation in our method is performed with the two-dimensional features of the data, and thus inherent spatial information of the data is fully exploited. This new type of two-dimensional discriminative regression, different from existing regression models, allows for building a highly effective and robust classifier for image data through explicitly incorporating discriminative information and inherent spatial information. We compare our method with several state-of-the-art classifiers of small-sample images and experimental results show superior performance of the proposed method in classification accuracy as well as robustness to noise corruption.
Bibliographical noteFunding Information:
This work is supported by National Natural Science Foundation of China (NSFC) under Grants 61806106 , 61802215 , and 61806045 , and Natural Science Foundation of Shandong Province under Grants ZR2019QF009 , and ZR2019BF011 ; Q.C. is partially supported by National Institute of Health (NIH) under Grants UH3 NS100606-03 and R21AG070909 , and a grant from the University of Kentucky .
© 2021 Elsevier B.V.
- Ridge regression
ASJC Scopus subject areas
- Management Information Systems
- Information Systems and Management
- Artificial Intelligence