Exclusive Lasso-Based k-Nearest Neighbors Classification

Qiu, Lin, Qu, Yanpeng, Shang, Changjing, Yang, Longzhi, Chao, Fei and Shen, Qiang (2021) Exclusive Lasso-Based k-Nearest Neighbors Classification. Neural Computing and Applications. ISSN 0941-0643 (In Press)

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Official URL: https://doi.org/10.1007/s00521-021-06069-5


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.

Item Type: Article
Additional Information: Funding information: This work was jointly supported by the Innovation Support Plan for Dalian High-level Talents (No. 2018RQ70) and partly by two awards under the Sêr Cymru II COFUND Fellowship scheme, UK.
Uncontrolled Keywords: Exclusive lasso, Sparse coefficien, kNN, Classification
Subjects: G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 28 Apr 2021 14:54
Last Modified: 31 Jul 2021 16:35
URI: http://nrl.northumbria.ac.uk/id/eprint/46047

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