# KNN Classification with One-step Computation

arXiv: https://arxiv.org/abs/2012.06047 · HTML: https://mlsanity.com/paper/2012.06047/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Mitigating Propensity Bias of Large Language Models for Recommender Systems](https://mlsanity.com/paper/2409.20052/) (2024)
- [Reachable Distance Function for KNN Classification](https://mlsanity.com/paper/2103.09704/) (2021)
- [MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction](https://mlsanity.com/paper/2510.15985/) (2025)
- [Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification](https://mlsanity.com/paper/2106.06873/) (2021)
- [DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification](https://mlsanity.com/paper/2510.12214/) (2025)
- [Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding](https://mlsanity.com/paper/2309.00816/) (2023)
- [Hashing Learning with Hyper-Class Representation](https://mlsanity.com/paper/2206.02334/) (2022)
- [Adaptive kNN graph model](https://mlsanity.com/paper/2601.16509/) (2026)

## Abstract (arXiv)

KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising