In this paper, we first offer an overview of advances in the field of distance metric learning.
Then, we empirically compare selected methods using a common experimental protocol …

Combining complementary information from multiple modalities is intuitively appealing for
improving the performance of learning-based approaches. However, it is challenging to fully …

Background: There is an urgent need for the development of global analytic frameworks that
can perform analyses in a privacy-preserving federated environment across multiple …

Identifying relationships between items is a key task of an online recommender system, in
order to help users discover items that are functionally complementary or visually …

Distance metric learning has been a hot research spot recently due to its high effectiveness
and efficiency in improving the performance of distance related methods, such as k nearest …

Meta-learning has emerged as an efficient approach for constructing target models based
on support sets. For example, the meta-learned embeddings enable the construction of …

For humans, it is usually easier to make statements about the similarity of objects in relative,
rather than absolute terms. Moreover, subjective comparisons of objects can be based on a …

Distance metric learning (DML) aims to find a suitable measure to compute a distance
between instances. Facilitated by side information, the learned metric can often improve the …

Linkages are essentially determined by similarity measures that may be derived from
multiple perspectives. For example, spatial linkages are usually generated based on …

Learning distance metric between objects provides a better measurement for their relative
comparisons. Due to the complex properties inside or between heterogeneous objects …