Incorporating Privileged Information Through Metric Learning
Incorporating Privileged Information Through Metric Learning
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DOI：
10.1109/tnnls.2013.2251470
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发表时间：
2013-07-01
影响因子：
10.4
通讯作者：
Schneider, Petra
中科院分区：
文献类型：
--
作者：
Fouad, Shereen;Tino, Peter;Schneider, Petra
在一些模式分析问题中，除了原始数据参与分类过程外，还存在专家知识。绝大多数现有的方法只是忽略了这种辅助（特权）知识。最近，一个新的范式学习使用特权信息的SVM+的框架中引入。这种方法是为二进制分类制定的，并且作为许多基于核的方法的典型，可以随着训练示例的数量而不利地扩展。虽然加快训练方法和SVM+的扩展到多类问题是可能的，在本文中，我们提出了一个更直接的新方法，将有价值的特权知识在模型构建阶段，主要制定在广义矩阵学习矢量量化的框架。这是通过基于特权信息所揭示的距离关系来改变输入空间中的全局度量来完成的。因此，与SVM+不同，在这种度量修改之后可以使用任何方便的分类器，从而为在训练期间合并特权信息的问题带来更大的灵活性。实验表明，基于特权数据的输入空间度量的操作提高了分类精度。此外，我们的方法可以实现竞争力的性能对SVM+配方。
In some pattern analysis problems, there exists expert knowledge, in addition to the original data involved in the classification process. The vast majority of existing approaches simply ignore such auxiliary (privileged) knowledge. Recently a new paradigm-learning using privileged information-was introduced in the framework of SVM+. This approach is formulated for binary classification and, as typical for many kernel-based methods, can scale unfavorably with the number of training examples. While speeding up training methods and extensions of SVM+ to multiclass problems are possible, in this paper we present a more direct novel methodology for incorporating valuable privileged knowledge in the model construction phase, primarily formulated in the framework of generalized matrix learning vector quantization. This is done by changing the global metric in the input space, based on distance relations revealed by the privileged information. Hence, unlike in SVM+, any convenient classifier can be used after such metric modification, bringing more flexibility to the problem of incorporating privileged information during the training. Experiments demonstrate that the manipulation of an input space metric based on privileged data improves classification accuracy. Moreover, our methods can achieve competitive performance against the SVM+ formulations.