LMNN

LMNN - Large Margin Nearest Neighbors: This is a MATLAB implementation of Large Margin Nearest Neighbor (LMNN), a metric learning algorithm first introduced by Kilian Q. Weinberger, John C. Blitzer and Lawrence K. Saul in 2005. LMNN is a metric learning algorithm to improve k-nearest neighbor classification by learning a generalized Euclidean metric Equation especially for nearest neighbor classification.


References in zbMATH (referenced in 82 articles , 1 standard article )

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  1. Rudin, Cynthia; Chen, Chaofan; Chen, Zhi; Huang, Haiyang; Semenova, Lesia; Zhong, Chudi: Interpretable machine learning: fundamental principles and 10 grand challenges (2022)
  2. Chu, Bryan; Farazmand, Mohammad: Data-driven prediction of multistable systems from sparse measurements (2021)
  3. Jin, Dequan; Qin, Ziyan; Yang, Murong; Chen, Penghe: A novel neural model with lateral interaction for learning tasks (2021)
  4. Le, Linh; Xie, Ying; Raghavan, Vijay V.: KNN loss and deep KNN (2021)
  5. Ma, Jiayi; Jiang, Xingyu; Fan, Aoxiang; Jiang, Junjun; Yan, Junchi: Image matching from handcrafted to deep features: a survey (2021)
  6. Ribeiro, Andre F.: Social media as author-audience games (2021)
  7. Ruan, Yibang; Xiao, Yanshan; Hao, Zhifeng; Liu, Bo: A nearest-neighbor search model for distance metric learning (2021)
  8. Shimada, Takuya; Bao, Han; Sato, Issei; Sugiyama, Masashi: Classification from pairwise similarities/dissimilarities and unlabeled data via empirical risk minimization (2021)
  9. Suárez, Juan Luis; García, Salvador; Herrera, Francisco: Ordinal regression with explainable distance metric learning based on ordered sequences (2021)
  10. Yoshida, Tomoki; Takeuchi, Ichiro; Karasuyama, Masayuki: Distance metric learning for graph structured data (2021)
  11. Zeng, Jingjing; Zou, Bin; Qin, Yimo; Chen, Qian; Xu, Jie; Yin, Lei; Jiang, Hongwei: Generalization ability of online pairwise support vector machine (2021)
  12. Chehreghani, Morteza Haghir: Unsupervised representation learning with minimax distance measures (2020)
  13. Dasgupta, Abhijit; Nayak, Losiana; Das, Ritankar; Basu, Debasis; Chandra, Preetam; De, Rajat K.: Pattern and rule mining for identifying signatures of epileptic patients from clinical EEG data (2020)
  14. de Vazelhes, William; Carey, Cj; Tang, Yuan; Vauquier, Nathalie; Bellet, Aurélien: metric-learn: metric learning algorithms in Python (2020)
  15. Hu, Ting; Fan, Jun; Xiang, Dao-Hong: Convergence analysis of distributed multi-penalty regularized pairwise learning (2020)
  16. Li, Haohao; Su, Zhixun; Li, Nannan; Liu, Ximin; Wang, Shengfa; Luo, Zhongxuan: Non-rigid 3D shape retrieval based on multi-scale graphical image and joint Bayesian (2020)
  17. Shen, Wei; Yang, Zhenhuan; Ying, Yiming; Yuan, Xiaoming: Stability and optimization error of stochastic gradient descent for pairwise learning (2020)
  18. Suárez, Juan Luis; García, Salvador; Herrera, Francisco: pyDML: a Python library for distance metric learning (2020)
  19. Nader, Rafic; Bretto, Alain; Mourad, Bassam; Abbas, Hassan: On the positive semi-definite property of similarity matrices (2019)
  20. Nguyen, Bac; Ferri, Francesc J.; Morell, Carlos; De Baets, Bernard: An efficient method for clustered multi-metric learning (2019)

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