Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

Multi-view learning (MVL) has attracted increasing attention and achieved great practical
success by exploiting complementary information of multiple features or modalities …

Deep metric learning papers from the past four years have consistently claimed great
advances in accuracy, often more than doubling the performance of decade-old methods. In …

Spectral clustering (SC) is a popular clustering technique to find strongly connected
communities on a graph. SC can be used in Graph Neural Networks (GNNs) to implement …

Optimising a ranking-based metric, such as Average Precision (AP), is notoriously
challenging due to the fact that it is non-differentiable, and hence cannot be optimised …

We propose a novel deep metric learning method by revisiting the learning to rank
approach. Our method, named FastAP, optimizes the rank-based Average Precision …

The objective of deep metric learning (DML) is to learn embeddings that can capture
semantic similarity information among data points. Existing pairwise or tripletwise loss …

Spectral clustering is a leading and popular technique in unsupervised data analysis. Two of
its major limitations are scalability and generalization of the spectral embedding (ie, out-of …

Recently, ensemble has been applied to deep metric learning to yield state-of-the-art results.
Deep metric learning aims to learn deep neural networks for feature embeddings, distances …

This paper presents a hardness-aware deep metric learning (HDML) framework. Most
previous deep metric learning methods employ the hard negative mining strategy to …