An overview and empirical comparison of distance metric learning methods
P Moutafis, M Leng, IA Kakadiaris - IEEE transactions on …, 2016 - ieeexplore.ieee.org
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 …
Then, we empirically compare selected methods using a common experimental protocol …
Learn to combine modalities in multimodal deep learning
Combining complementary information from multiple modalities is intuitively appealing for
improving the performance of learning-based approaches. However, it is challenging to fully …
improving the performance of learning-based approaches. However, it is challenging to fully …
[HTML][HTML] Privacy-preserving patient similarity learning in a federated environment: development and analysis
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 …
can perform analyses in a privacy-preserving federated environment across multiple …
Learning compatibility across categories for heterogeneous item recommendation
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 …
order to help users discover items that are functionally complementary or visually …
Survey and experimental study on metric learning methods
D Li, Y Tian - Neural networks, 2018 - Elsevier
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 …
and efficiency in improving the performance of distance related methods, such as k nearest …
Multiview triplet embedding: Learning attributes in multiple maps
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 …
rather than absolute terms. Moreover, subjective comparisons of objects can be based on a …
Contextualizing meta-learning via learning to decompose
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 …
on support sets. For example, the meta-learned embeddings enable the construction of …
Fast generalization rates for distance metric learning: Improved theoretical analysis for smooth strongly convex distance metric learning
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 …
between instances. Facilitated by side information, the learned metric can often improve the …
What makes objects similar: A unified multi-metric learning approach
Linkages are essentially determined by similarity measures that may be derived from
multiple perspectives. For example, spatial linkages are usually generated based on …
multiple perspectives. For example, spatial linkages are usually generated based on …
Self-guided deep multi-view subspace clustering network
B Cui, H Yu, L Zong, Z Cheng - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
To cluster the data with complex structures, Deep Subspace Clustering Network (DSCN)
extracts the subspace relations among non-linear latent features. However, the performance …
extracts the subspace relations among non-linear latent features. However, the performance …