Affiliations: [a] School of Physics and Information Engineering, Jiangsu Second Normal University, Nanjing, China | [b] Jiangsu Province Engineering Research Center of Basic Education Big Data Application, Nanjing, China | [c] School of computer science and technology, China University of mining and technology, Xuzhou, China | [d] Jiangsu Vocational Institute of Architectural Technology, Jiangsu Collaborative Innovation Center for Building Energy Saving and Construct Technology, Xuzhou, China
Note: [*] This author and first author contribute equally to this work.
Abstract: In this paper, a novel approach for transductive classification is proposed. Unlike existing methods that heavily rely on constructing the Laplacian matrix to capture data distribution, the proposed approach takes a unique path. It employs a linear transformation model to create local patches for each data point and then unifies them in an objective function to build the Laplacian matrix. Incorporating this Laplacian matrix into the transductive classification framework allows us to assign optimal class labels globally. The experimental results from toy data and real world databases demonstrate that the proposed approach achieves more efficient and stable performance, while this approach is insensitive to the parameters. Notably, our method exhibits robustness to parameter variations, making it highly adaptable to practical applications.
