Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume -, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3185408
Keywords
Graph diffusion; graph embedding; linear regression (LR); local structure
Categories
Funding
- National Natural Science Foundation of China [62006059, 62176066]
- Shenzhen Fundamental Research Fund [JCYJ20190806142416685]
- Shenzhen Science and Technology Program [RCBS20210609103709020]
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In this article, a new linear regression-based multiclass classification method, called DRAGD, is proposed. It explores the high-order structure information and provides a new way to capture the structure of data, resulting in a more discriminative transformation matrix. Experimental results show that DRAGD outperforms existing LR methods.
In this article, we propose a new linear regression (LR)-based multiclass classification method, called discriminative regression with adaptive graph diffusion (DRAGD). Different from existing graph embedding-based LR methods, DRAGD introduces a new graph learning and embedding term, which explores the high-order structure information between four tuples, rather than conventional sample pairs to learn an intrinsic graph. Moreover, DRAGD provides a new way to simultaneously capture the local geometric structure and representation structure of data in one term. To enhance the discriminability of the transformation matrix, a retargeted learning approach is introduced. As a result of combining the above-mentioned techniques, DRAGD can flexibly explore more unsupervised information underlying the data and the label information to obtain the most discriminative transformation matrix for multiclass classification tasks. Experimental results on six well-known real-world databases and a synthetic database demonstrate that DRAGD is superior to the state-of-the-art LR methods.
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