We propose a novel distance-based regularization method for deep metric learning called Multi-level Distance Regularization (MDR). MDR explicitly disturbs procedure by regularizing pairwise distances between embedding vectors into multiple levels that represents degree of similarity pair. In the training stage, model is trained with both and an existing loss function learning, simultaneously; t...

A lot of machine learning algorithms are based on metric functions, which good functions lead to better results. Distance metric learning has been widely attracted by researchers in last decade. Kernel matrix is somehow a distance function which indicates the similarity between two instances in the feature space which contains high dimensions. Traditional distance metric learning approaches are...

The metric learning problem is concerned with learning a distance function tuned to a particular task, and has been shown to be useful when used in conjunction with nearest-neighbor methods and other techniques that rely on distances or similarities. This survey presents an overview of existing research in metric learning, including recent progress on scaling to high-dimensional feature spaces ...

Distance Metric Learning (Dml) aims to find a distance metric, revealing feature relationship and satisfying restrictions between instances, for distance based classifiers, e.g., kNN. Most Dml methods take all features into consideration while leaving the feature importance identification untouched. Feature selection methods, on the other hand, only focus on feature weights and are seldom direc...

recently, cho et al. [y. j. cho, r. saadati, s. h. wang, common xed point theorems on generalized distance in ordered cone metric spaces, comput. math. appl. 61 (2011) 1254-1260] dened the concept of the c-distance in a cone metric space and proved some xed point theorems on c-distance. in this paper, we prove some new xed point and common xed point theorems by using the distance in ordere...

Recently, Cho et al. [Y. J. Cho, R. Saadati, S. H. Wang, Common xed point theorems on generalized distance in ordered cone metric spaces, Comput. Math. Appl. 61 (2011) 1254-1260] dened the concept of the c-distance in a cone metric space and proved some xed point theorems on c-distance. In this paper, we prove some new xed point and common xed point theorems by using the distance in ordered con...

Traditional distance metric learning with side information usually formulates the objectives using the covariance matrices of the data point pairs in the two constraint sets of must-links and cannotlinks. Because the covariance matrix computes the sum of the squared l2-norm distances, it is prone to both outlier samples and outlier features. To develop a robust distance metric learning method, ...