The goal in extreme multi-label classification is to learn a classifier which
can assign a small subset of relevant labels to an instance from an extremely
large set of target labels. Datasets in extreme classification exhibit a long
tail of labels which have small number of positive training instances. In this
work, we pose the learning task in extreme classification with large number of
tail-labels as learning in the presence of adversarial perturbations. This view
motivates a robust optimization framework and equivalence to a corresponding
regularized objective.
Under the proposed robustness framework, we demonstrate efficacy of Hamming
loss for tail-label detection in extreme classification. The equivalent
regularized objective, in combination with proximal gradient based
optimization, performs better than state-of-the-art methods on propensity
scored versions of precision@k and nDCG@k(upto 20% relative improvement over
PFastreXML - a leading tree-based approach and 60% relative improvement over
SLEEC - a leading label-embedding approach). Furthermore, we also highlight the
sub-optimality of a sparse solver in a widely used package for large-scale
linear classification, which is interesting in its own right. We also
investigate the spectral properties of label graphs for providing novel
insights towards understanding the conditions governing the performance of
Hamming loss based one-vs-rest scheme vis-\`a-vis label embedding methods.

本文探讨了在存在对抗扰动的情况下的极端分类中的学习任务，并提出了一种鲁棒优化框架和相应的正则化目标，发现 Hamming 损失可作为尾标签检测的有效方法。通过结合正则化目标和基于 Proximal 梯度的优化方法，相对于 PFastreXML 和 SLEEC 等现有方法在 precision@k 和 nDCG@k 上提供了优异的性能。同时，我们还研究了标签图的谱特性，提供了新的洞察力以了解 Hamming 损失的一对多方法和标签嵌入方法之间性能的条件限制。

对抗性极端多标签分类

Adversarial Extreme Multi-label Classification

Images of scenes have various objects as well as abundant attributes, and
diverse levels of visual categorization are possible. A natural image could be
assigned with fine-grained labels that describe major components,
coarse-grained labels that depict high level abstraction or a set of labels
that reveal attributes. Such categorization at different concept layers can be
modeled with label graphs encoding label information. In this paper, we exploit
this rich information with a state-of-art deep learning framework, and propose
a generic structured model that leverages diverse label relations to improve
image classification performance. Our approach employs a novel stacked label
prediction neural network, capturing both inter-level and intra-level label
semantics. We evaluate our method on benchmark image datasets, and empirical
results illustrate the efficacy of our model.

本文提出一种基于标签图和深度学习的结构化模型，采用新型的堆叠式标签预测神经网络，并利用不同概念层次的标签关系来提高图像分类性能，在基准图像数据集上验证了该方法的有效性。