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Self-paced and Bayes-decision-rule linear KNN prediction

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Abstract

While a testing sample may be first encoded linearly with labeled samples and then classified with KNN on the sum of the obtained weights of the samples in each class so as to avoid the consistent distribution assumption explicitly or implicitly used in most of the existing classification methods for training and testing samples, a novel self-paced and Bayes-decision-rule linear KNN prediction method SBLD-KNN in this study will be proposed to achieve three goals: (1) class-ware information will be explicitly reflected in a grouping effect regularization term so as to share the sparsity of a linear encoder and simultaneously have grouping effect of weights on each class; (2) the resultant predictor behaves like Bayes-decision-rule for minimum error; (3) self-paced regularized term is designed to adaptively truncate the weights of labeled samples for enhancing generalization. In order to do so, the corresponding objective function of SBLD-KNN is designed and then optimized by using the alternating optimization strategy, and its Bayes-decision-rule is theoretically analyzed. Our experimental results on benchmark datasets witness the effectiveness of SBLD-KNN, in contrast to the comparative methods, including SBLD-KNN’s simplified version BD-KNN with weight’s truncating rather than self-pacing.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants U20A20228, 61772198, 6197071117, by the Natural Science Foundation of Jiangsu Province under Grant BK20191331, and by National First-class Discipline Program of Light Industry and Engineering (LITE2018). (Corresponding author: Shitong Wang). (Jin Zhang and Zekang Bian have equal contribution to this work).

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Authors and Affiliations

  1. School of AI and Computer Science, Jiangnan University, Wuxi, Jiangsu, China

    Jin Zhang, Zekang Bian & Shitong Wang

  2. Taihu Jiangsu Key Construction Lab. of IoT Application Technologies, Wuxi, China

    Jin Zhang, Zekang Bian & Shitong Wang

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  1. Jin Zhang
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  2. Zekang Bian
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  3. Shitong Wang
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Correspondence to Shitong Wang.

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Zhang, J., Bian, Z. & Wang, S. Self-paced and Bayes-decision-rule linear KNN prediction. Int. J. Mach. Learn. & Cyber. 13, 3267–3283 (2022). https://doi.org/10.1007/s13042-022-01593-9

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  • DOI: https://doi.org/10.1007/s13042-022-01593-9

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