scholarly journals Manifold Regularized Transfer Distance Metric Learning

2015 ◽  
Author(s):  
Haibo Shi ◽  
Yong Luo ◽  
Chao Xu ◽  
Yonggang Wen
Keyword(s):  
Metric Learning ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Transfer Distance

scholarly journals General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer

2017 ◽  
Author(s):  
Yong Luo ◽  
Yonggang Wen ◽  
Tongliang Liu ◽  
Dacheng Tao
Keyword(s):  
Transfer Learning ◽  
Metric Learning ◽  
Learning Task ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Target Domain ◽  
Feature Representations ◽  
Transfer Distance ◽  
Learning Tasks ◽  
Feature Spaces

Transfer learning aims to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. Recently, transfer distance metric learning (TDML) has attracted lots of interests, but most of these methods assume that feature representations for the source and target learning tasks are the same. Hence, they are not suitable for the applications, in which the data are from heterogeneous domains (feature spaces, modalities and even semantics). Although some existing heterogeneous transfer learning (HTL) approaches is able to handle such domains, they lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. We therefore develop a general and flexible heterogeneous TDML (HTDML) framework based on the knowledge fragment transfer strategy. In the proposed HTDML, any (linear or nonlinear) distance metric learning algorithms can be employed to learn the source metric beforehand. Then a set of knowledge fragments are extracted from the pre-learned source metric to help target metric learning. In addition, either linear or nonlinear distance metric can be learned for the target domain. Extensive experiments on both scene classification and object recognition demonstrate superiority of the proposed method.

scholarly journals Distance metric learning for graph structured data

2021 ◽  
Author(s):  
Tomoki Yoshida ◽  
Ichiro Takeuchi ◽  
Masayuki Karasuyama
Keyword(s):  
Metric Learning ◽  
Structured Data ◽  
Distance Metric Learning ◽  
Distance Metric

scholarly journals Chi-Squared Distance Metric Learning for Histogram Data

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Yang ◽  
Luhui Xu ◽  
Xiaopan Chen ◽  
Fengbin Zheng ◽  
Yang Liu
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Metric Learning ◽  
Nearest Neighbors ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Projected Gradient Method ◽  
Proper Distance ◽  
Chi Squared ◽  
Real World Datasets

Learning a proper distance metric for histogram data plays a crucial role in many computer vision tasks. The chi-squared distance is a nonlinear metric and is widely used to compare histograms. In this paper, we show how to learn a general form of chi-squared distance based on the nearest neighbor model. In our method, the margin of sample is first defined with respect to the nearest hits (nearest neighbors from the same class) and the nearest misses (nearest neighbors from the different classes), and then the simplex-preserving linear transformation is trained by maximizing the margin while minimizing the distance between each sample and its nearest hits. With the iterative projected gradient method for optimization, we naturally introduce thel2,1norm regularization into the proposed method for sparse metric learning. Comparative studies with the state-of-the-art approaches on five real-world datasets verify the effectiveness of the proposed method.

Out-of-Distribution Detection Based on Distance Metric Learning

2020 ◽  
Author(s):  
Donghun Yang ◽  
Iksoo Shin ◽  
Mai Ngoc Kien ◽  
Hoyong Kim ◽  
Chanhee Yu ◽  
...  
Keyword(s):  
Metric Learning ◽  
Distance Metric Learning ◽  
Distance Metric
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