
Metric Learning for Ordered Labeled Trees with pqgrams
Computing the similarity between two data points plays a vital role in m...
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Improving Performance of SelfOrganising Maps with Distance Metric Learning Method
SelfOrganising Maps (SOM) are Artificial Neural Networks used in Patter...
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MLAS: Metric Learning on Attributed Sequences
Distance metric learning has attracted much attention in recent years, w...
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Graph Neural Distance Metric Learning with GraphBert
Graph distance metric learning serves as the foundation for many graph l...
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Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement
Distance metric learning can be viewed as one of the fundamental interes...
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Transfer Metric Learning: Algorithms, Applications and Outlooks
Distance metric learning (DML) aims to find an appropriate way to reveal...
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Metric Learning for Phoneme Perception
Metric functions for phoneme perception capture the similarity structure...
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Learning an Integrated Distance Metric for Comparing Structure of Complex Networks
Graph comparison plays a major role in many network applications. We often need a similarity metric for comparing networks according to their structural properties. Various network features  such as degree distribution and clustering coefficient  provide measurements for comparing networks from different points of view, but a global and integrated distance metric is still missing. In this paper, we employ distance metric learning algorithms in order to construct an integrated distance metric for comparing structural properties of complex networks. According to natural witnesses of network similarities (such as network categories) the distance metric is learned by the means of a dataset of some labeled real networks. For evaluating our proposed method which is called NetDistance, we applied it as the distance metric in Knearestneighbors classification. Empirical results show that NetDistance outperforms previous methods, at least 20 percent, with respect to precision.
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