… We provide a new intuitive interpretation for traditional linear metric learning by explicitly …
a generalized metric learning model dubbed CDML with discovering the curvilinear distance …

… In this paper, we study the curvilinear metric learning for … CCML solves the geometric
problems that existing linear metric learning … RELATED WORK CCML is a metric learning based …

Distance metric learning (DML) aims to learn a distance metric to process the data
distribution. … Specifically, we learn a specific Mahalanobis distance metric from the kernel-transformed …

… tree (MST) clustering with distance metric learning. Given a distance metric, the connected
… By learning the distance metric in supervised learning on a dataset of pairs of CCs, the …

… At the first glance, the curvilinear distance appears as a very strange concept. Indeed, …
distance, the curvilinear distance depends not only on the two points between which the distance …

… Adaptive distance metric learning based on the characteristics of data can significantly … of
single metric learning for heterogeneous data, multiple local metric learning has become an …

… This paper compares two nonlinear projection methods: Isomap and Curvilinear Distance
Analysis. Contrarily to the traditional linear PCA, these methods work like multidimensional …

… by reformulating the standard proxy-based metric learning approach to a distributional one
on … (3) We introduce various distribution-to-distribution metrics for DML and contrast them to …

… , locally linear metric adaptation, and adversarial metric learning are covered. We also … for
metric learning in the feature space. We also introduce geometric metric learning methods on …

… review of Riemannian metric learning, a generalization of metric learning that has recently
… • We present Riemannian metric learning as a generalization of distance metric learning …