普适相似性
Universal Similarity
摘要：
我们调查了一个新的领域的无参数的相似性距离测量有用的数据挖掘，模式识别，学习和自动语义提取。给定一组对象上的一组距离，如果距离将集合中每两个对象之间的族中的每个距离最小化到指定的精度，则距离对于该族来说在一定精度下是通用的（我们不要求通用距离成为族的一个元素）。我们考虑两种类型的对象的相似性距离：文字对象，本身包含它们的所有含义，如基因组或书籍，以及对象的名称。后者可能像第一种类型一样有字面上的体现，但也可能像“红色”或“基督教”那样抽象。对于第一种类型，我们考虑一族可计算的距离度量，其对应于根据文字对象对之间的特定特征表达相似性的参数。对于第二种类型，我们认为相似性距离所产生的Web用户对应的特定语义关系（名称）的指定对象。对于这两个家庭，我们给普遍的相似性距离措施，将所有特定的距离措施的家庭。在第一种情况下，通用距离基于压缩，在第二种情况下，它基于与搜索词相关的Google页面计数。在这两种情况下，大规模的实验证明了这些方法的可行性。
英文摘要：
We survey a new area of parameter-free similarity distance measures useful in data-mining, pattern recognition, learning and automatic semantics extraction. Given a family of distances on a set of objects, a distance is universal up to a certain precision for that family if it minorizes every distance in the family between every two objects in the set, up to the stated precision (we do not require the universal distance to be an element of the family). We consider similarity distances for two types of objects: literal objects that as such contain all of their meaning, like genomes or books, and names for objects. The latter may have literal embodyments like the first type, but may also be abstract like ``red'' or ``christianity.'' For the first type we consider a family of computable distance measures corresponding to parameters expressing similarity according to particular features between pairs of literal objects. For the second type we consider similarity distances generated by web users corresponding to particular semantic relations between the (names for) the designated objects. For both families we give universal similarity distance measures, incorporating all particular distance measures in the family. In the first case the universal distance is based on compression and in the second case it is based on Google page counts related to search terms. In both cases experiments on a massive scale give evidence of the viability of the approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
