Regressive Virtual Metric Learning
نویسندگان
چکیده
We are interested in supervised metric learning of Mahalanobis like distances. Existing approaches mainly focus on learning a new distance using similarity and dissimilarity constraints between examples. In this paper, instead of bringing closer examples of the same class and pushing far away examples of different classes we propose to move the examples with respect to virtual points. Hence, each example is brought closer to a a priori defined virtual point reducing the number of constraints to satisfy. We show that our approach admits a closed form solution which can be kernelized. We provide a theoretical analysis showing the consistency of the approach and establishing some links with other classical metric learning methods. Furthermore we propose an efficient solution to the difficult problem of selecting virtual points based in part on recent works in optimal transport. Lastly, we evaluate our approach on several state of the art datasets.
منابع مشابه
The Effect of Training in Virtual Environment on Nursing Students Attitudes toward Virtual Learning and its Relationship with Learning Style
Introduction: It is impossible to be successful in virtual training unless we consider individuals’ viewpoints toward it. Despite this fact, less attention has been paid to students’ attitudes at the end of a virtual course in the published studies. This study investigates the effect of a virtual training course on the students` attitudes toward virtual education and its relationship with learn...
متن کاملComposite Kernel Optimization in Semi-Supervised Metric
Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...
متن کاملAn Effective Approach for Robust Metric Learning in the Presence of Label Noise
Many algorithms in machine learning, pattern recognition, and data mining are based on a similarity/distance measure. For example, the kNN classifier and clustering algorithms such as k-means require a similarity/distance function. Also, in Content-Based Information Retrieval (CBIR) systems, we need to rank the retrieved objects based on the similarity to the query. As generic measures such as ...
متن کاملStudy of the Factors Affecting on the Quality of Virtual Learning and Education from the Perspective of Dental Students of Zahedan University of Medical Sciences
Abstract Background & Aim: Nowadays the use of digital technology in dental education hasdramatically increased. The growth and development of educational models shows that it is important to change the approach of education from traditional methods to electronic methods and virtual education. We conducted the present study in aims of investigating the effective factors on the quality of vir...
متن کاملInvestigating the Impact of Virtual Social Networks on Social Capital and Organizational Learning Capabilities with the Mediating Role of Helpful Activities
Introduction: The main topic of this research is to Investigating the Impact of Virtual Social Networks on Social Capital and Organizational Learning Capabilities with the Mediating Role of Helpful Activities. An important feature of social networks is that it has become a place to share knowledge, which in turn contributes to the quantitative and qualitative improvement of social capital. Thus...
متن کامل