For many tasks and data types, there are natural transformations to which the
data should be invariant or insensitive. For instance, in visual recognition,
natural images should be insensitive to rotation and translation. This
requirement and its implications have been important in many machine learning
applications, and tolerance for image transformations was primarily achieved by
using robust feature vectors. In this paper we propose a novel and
computationally efficient way to learn a local Mahalanobis metric per datum,
and show how we can learn a local invariant metric to any transformation in
order to improve performance.

本文提出了一种新颖且计算效率高的学习本地马氏度量的方法，并展示了如何学习任何转换的本地不变度量来提高性能。

学习局部不变的马氏距离

Learning Local Invariant Mahalanobis Distances

This paper investigates domain generalization: How to take knowledge acquired
from an arbitrary number of related domains and apply it to previously unseen
domains? We propose Domain-Invariant Component Analysis (DICA), a kernel-based
optimization algorithm that learns an invariant transformation by minimizing
the dissimilarity across domains, whilst preserving the functional relationship
between input and output variables. A learning-theoretic analysis shows that
reducing dissimilarity improves the expected generalization ability of
classifiers on new domains, motivating the proposed algorithm. Experimental
results on synthetic and real-world datasets demonstrate that DICA successfully
learns invariant features and improves classifier performance in practice.

本文介绍了一种基于核方法的算法 Domain-Invariant Component Analysis，通过最小化域间差异并保留输入输出间的功能关系，学习一个不变转换来实现从已知相关领域到未曾接触领域的知识迁移。学习理论分析表明，减少差异可以提高分类器在新领域上的期望泛化能力。实验结果表明，DICA 成功地学习了不变的特征并改善了分类器的性能。