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 67 articles , 1 standard article )

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  1. Chehreghani, Morteza Haghir: Unsupervised representation learning with minimax distance measures (2020)
  2. de Vazelhes, William; Carey, Cj; Tang, Yuan; Vauquier, Nathalie; Bellet, Aurélien: metric-learn: metric learning algorithms in Python (2020)
  3. Hu, Ting; Fan, Jun; Xiang, Dao-Hong: Convergence analysis of distributed multi-penalty regularized pairwise learning (2020)
  4. 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)
  5. Shen, Wei; Yang, Zhenhuan; Ying, Yiming; Yuan, Xiaoming: Stability and optimization error of stochastic gradient descent for pairwise learning (2020)
  6. Suárez, Juan Luis; García, Salvador; Herrera, Francisco: pyDML: a Python library for distance metric learning (2020)
  7. Nader, Rafic; Bretto, Alain; Mourad, Bassam; Abbas, Hassan: On the positive semi-definite property of similarity matrices (2019)
  8. Nguyen, Bac; Ferri, Francesc J.; Morell, Carlos; De Baets, Bernard: An efficient method for clustered multi-metric learning (2019)
  9. Nuti, Giuseppe: An efficient algorithm for Bayesian nearest neighbours (2019)
  10. Rayhan, Farshid; Ahmed, Sajid; Md Farid, Dewan; Dehzangi, Abdollah; Shatabda, Swakkhar: CFSBoost: cumulative feature subspace boosting for drug-target interaction prediction (2019)
  11. Ting, Kai Ming; Zhu, Ye; Carman, Mark; Zhu, Yue; Washio, Takashi; Zhou, Zhi-Hua: Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms (2019)
  12. Ye, Han-Jia; Zhan, De-Chuan; Jiang, Yuan: Fast generalization rates for distance metric learning. Fast generalization rates for distance metric learning, improved theoretical analysis for smooth strongly convex distance metric learning (2019)
  13. Yoshida, Tomoki; Takeuchi, Ichiro; Karasuyama, Masayuki: Safe triplet screening for distance metric learning (2019)
  14. Zhang, Weifeng; Hu, Hua; Hu, Haiyang; Fang, Jinglong: Semantic distance between vague concepts in a framework of modeling with words (2019)
  15. Zhu, Rui; Dong, Mingzhi; Xue, Jing-Hao: Learning distance to subspace for the nearest subspace methods in high-dimensional data classification (2019)
  16. Flamary, Rémi; Cuturi, Marco; Courty, Nicolas; Rakotomamonjy, Alain: Wasserstein discriminant analysis (2018)
  17. Kang, Bo; Lijffijt, Jefrey; Santos-Rodríguez, Raúl; De Bie, Tijl: SICA: subjectively interesting component analysis (2018)
  18. Pu, Yu-Chi; You, Pei-Chun: Indoor positioning system based on BLE location fingerprinting with classification approach (2018)
  19. Yu, Panpan; Li, Qingna: Ordinal distance metric learning with MDS for image ranking (2018)
  20. Zhao, Wentao; Li, Qian; Zhu, Chengzhang; Song, Jianglong; Liu, Xinwang; Yin, Jianping: Model-aware categorical data embedding: a data-driven approach (2018)

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