Exclusive lasso-based k-nearest-neighbor classification

Abstract

Conventionally, the k nearest-neighbor (kNN) classification is implemented with the use of the Euclidean distance-based measures, which are mainly the one-to-one similarity relationships such as to lose the connections between different samples. As a strategy to alleviate this issue, the coefficients coded by sparse representation have played a role of similarity gauger for nearest-neighbor classification as well. Although SR coefficients enjoy remarkable discrimination nature as a one-to-many relationship, it carries out variable selection at the individual level so that possible inherent group structure is ignored. In order to make the most of information implied in the group structure, this paper employs the exclusive lasso strategy to perform the similarity evaluation in two novel nearest-neighbor classification methods. Experimental results on both benchmark data sets and the face recognition problem demonstrate that the EL-based kNN method outperforms certain state-of-the-art classification techniques and existing representation-based nearest-neighbor approaches, in terms of both the size of feature reduction and the classification accuracy.

Local representation‐based neighbourhood for robust classification

“…The work in Zheng and Ding (2020) incorporates group lasso into classification by using it for the selection of the most relevant groups to improve the robustness of . By using the exclusive lasso regularisation (Zhou et al, 2010), the implicit information in the group structure is fully utilised, thereby enhancing the performance of the nearest neighbour method in the classification task as reported in Qiu et al (2021). What is more, a group collaborative representation model is proposed in (Liu et al, 2019) to capture the hidden inter‐ and intra‐set structure between images.…”