A self-adaptive local metric learning method for classification
作者：
Highlights：
• In this paper, a new framework named SA-LM2 in supervised local distance metric learning is introduced. In this framework, learning an appropriate distance metric and finding the local neighborhoods are integrated in a joint formulation.
• Unlike other existing algorithms, where they need to select k nearest neighbors, SA-LM2 learns the radius of local neighbourhoods automatically.
• SA-LM2 is expressed as a semidefinite programming, where due to its convex nature it avoids the local optima and is of global convergence guarantee.
• Only the dissimilar set D is applied in SA-LM2, which makes it useful in some application where we do not have any prior knowledge about the similar data.
• The results of SA-LM2 are less influenced by noisy input data points than the other compared global and local algorithms.
摘要
•In this paper, a new framework named SA-LM2 in supervised local distance metric learning is introduced. In this framework, learning an appropriate distance metric and finding the local neighborhoods are integrated in a joint formulation.•Unlike other existing algorithms, where they need to select k nearest neighbors, SA-LM2 learns the radius of local neighbourhoods automatically.•SA-LM2 is expressed as a semidefinite programming, where due to its convex nature it avoids the local optima and is of global convergence guarantee.•Only the dissimilar set D is applied in SA-LM2, which makes it useful in some application where we do not have any prior knowledge about the similar data.•The results of SA-LM2 are less influenced by noisy input data points than the other compared global and local algorithms.
论文关键词：Supervised metric learning,Linear metric learning,Local algorithm,Neighborhood learning,Multimodal data
论文评审过程：Received 1 April 2019, Revised 19 July 2019, Accepted 31 July 2019, Available online 2 August 2019, Version of Record 8 August 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.106994
