scholarly journals Generative Local Metric Learning for Nearest Neighbor Classification

2018 ◽  
Vol 40 (1) ◽  
pp. 106-118 ◽  
Author(s):  
Yung-Kyun Noh ◽  
Byoung-Tak Zhang ◽  
Daniel D. Lee
Keyword(s):  
Nearest Neighbor ◽  
Metric Learning ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

HIERARCHICAL DISTANCE METRIC LEARNING FOR LARGE MARGIN NEAREST NEIGHBOR CLASSIFICATION

2011 ◽  
Vol 25 (07) ◽  
pp. 1073-1087 ◽  
Author(s):  
SHILIANG SUN ◽  
QIAONA CHEN
Keyword(s):  
Nearest Neighbor ◽  
Metric Learning ◽  
Data Sets ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Real World Data ◽  
K Nearest Neighbors ◽  
Large Margin ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification

Distance metric learning is a powerful tool to improve performance in classification, clustering and regression tasks. Many techniques have been proposed for distance metric learning based on convex programming, kernel learning, dimension reduction and large margin. The recently proposed large margin nearest neighbor classification (LMNN) improves the performance of k-nearest neighbors classification (k-nn) by a learned global distance metric. However, it does not consider the locality of data distributions. We demonstrate a novel local distance metric learning method called hierarchical distance metric learning (HDM) which first builds a hierarchical structure by grouping data points according to the overlapping ratios defined by us and then learns distance metrics sequentially. In this paper, we combine HDM with LMNN and further propose a new method named hierarchical distance metric learning for large margin nearest neighbor classification (HLMNN). Experiments are performed on many artificial and real-world data sets. Comparisons with the traditional k-nn and the state-of-the-art LMNN show the effectiveness of the proposed HLMNN.

scholarly journals Margin distribution explanation on metric learning for nearest neighbor classification

2016 ◽  
Vol 177 ◽  
pp. 168-178 ◽  
Author(s):  
Peng-Cheng Zou ◽  
Jiandong Wang ◽  
Songcan Chen ◽  
Haiyan Chen
Keyword(s):  
Nearest Neighbor ◽  
Metric Learning ◽  
Nearest Neighbor Classification ◽  
Margin Distribution ◽  
Neighbor Classification

scholarly journals Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling

2021 ◽  
Vol 6 (1) ◽  
pp. 1-5
Author(s):  
Parisa Abdolrahim Poorheravi ◽  
Benyamin Ghojogh ◽  
Vincent Gaudet ◽  
Fakhri Karray ◽  
Mark Crowley
Keyword(s):  
Nearest Neighbor ◽  
Metric Learning ◽  
Stratified Sampling ◽  
Hierarchical Approach ◽  
Large Margin ◽  
Nearest Neighbor Classification ◽  
Triplet Loss ◽  
Mining Methods ◽  
Siamese Networks ◽  
Neighbor Classification

Metric learning is a technique in manifold learning to find a projection subspace for increasing and decreasing the inter- and intra-class variances, respectively. Some metric learning methods are based on triplet learning with anchor-positive-negative triplets. Large margin metric learning for nearest neighbor classification is one of the fundamental methods to do this. Recently, Siamese networks have been introduced with the triplet loss. Many triplet mining methods have been developed for Siamese nets; however, these techniques have not been applied on the triplets of large margin metric learning. In this work, inspired by the mining methods for Siamese nets, we propose several triplet mining techniques for large margin metric learning. Moreover, a hierarchical approach is proposed, for acceleration and scalability of optimization, where triplets are selected by stratified sampling in hierarchical hyper-spheres. We analyze the proposed methods on three publicly available datasets.

scholarly journals Exclusive lasso-based k-nearest-neighbor classification

2021 ◽  
Author(s):  
Lin Qiu ◽  
Yanpeng Qu ◽  
Changjing Shang ◽  
Longzhi Yang ◽  
Fei Chao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classification ◽  
Neighbor Classification
Sign in / Sign up

Export Citation Format

Share Document