Low-rank geometric mean metric learning
We propose a low-rank approach to learning a Mahalanobis metric from data. Inspired by the recent geometric mean metric learning (GMML) algorithm, we propose a low-rank variant of the algorithm. This allows to jointly learn a low-dimensional subspace where the data reside and the Mahalanobis metric that appropriately fits the data. Our results show that we compete effectively with GMML at lower ranks.
Code (1)
Tasks
Metric LearningSimilar Papers 제목 키워드 기반
Multivariate Spearman's rho for aggregating ranks using copulas
We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known.…
Learning-To-RankDeep geometric matrix completion: Are we doing it right?
We address the problem of reconstructing a matrix from a subset of its entries. Current methods, branded as geometric matrix completion, augment classical rank regularization techniques by incorporating geometric informa…
Matrix CompletionRecommendation SystemsLearning to Rank Using Localized Geometric Mean Metrics
Many learning-to-rank (LtR) algorithms focus on query-independent model, in which query and document do not lie in the same feature space, and the rankers rely on the feature ensemble about query-document pair instead of…
Computational EfficiencyLearning-To-RankMetric LearningDiffKendall: A Novel Approach for Few-Shot Learning with Differentiable Kendall's Rank Correlation
Few-shot learning aims to adapt models trained on the base dataset to novel tasks where the categories were not seen by the model before. This often leads to a relatively uniform distribution of feature values across cha…
Few-Shot Image ClassificationFew-Shot LearningGeometric Deep Learning for Molecular Crystal Structure Prediction
We develop and test new machine learning strategies for accelerating molecular crystal structure ranking and crystal property prediction using tools from geometric deep learning on molecular graphs. Leveraging developmen…
Deep LearningPredictionProperty Prediction