Dissimilarity representation is a very interesting alternative for the traditional feature space representation when addressing large multi-class problems or even problems with a small number of training samples. This paper describes the existing possibilities in terms of dissimilarity representation through some comprehensive examples. The justification for using such a problem representation strategy is discussed, followed by a complete review of the state-of-art and a critical analysis in which the original purpose of the dissimilarity representation and its perspectives are discussed. Dissimilarity space derived from automatically learned features and the possibility of transiting from one space to another when performing the tasks of the classification process are good examples of promising research directions in this field.

中文翻译：

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差异性方法：回顾

在解决大型多类问题甚至是少量训练样本的问题时，相异表示是传统特征空间表示的一个非常有趣的替代方案。本文通过一些综合示例描述了在相异性表示方面的现有可能性。讨论了使用这种问题表示策略的理由，然后是对现有技术的完整回顾和批判性分析，其中讨论了相异性表示的原始目的及其观点。自动学习特征派生的差异空间以及在执行分类过程任务时从一个空间转换到另一个空间的可能性是该领域有前途的研究方向的很好例子。