Similarity of Objects and the Meaning of Words

We survey the emerging area of compression-based, 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 act 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 sim...

• Type: Journal
• Year: 2006
• Where: CORR
• Authors: Rudi Cilibrasi, Paul M. B. Vitányi
• Keywords: CORR 2006, Education, Literal Objects, Similarity Distance, Universal