Normalized Compression Distance of Multisets with Applications.
Pairwise normalized compression distance (NCD) is a parameter-free, feature-free, alignment-free, similarity metric based on compression. We propose an NCD of multisets that is also metric. Previously, attempts to obtain such an NCD failed. For classification purposes it is superior to the pairwise NCD in accuracy and implementation complexity. We cover the entire trajectory from theoretical underpinning to feasible practice. It is applied to biological (stem cell, organelle transport) and OCR classification questions that were earlier treated with the pairwise NCD. With the new method we achieved significantly better results. The theoretic foundation is Kolmogorov complexity.
Andrew R
AR
Cohen
Paul M B
PM
Vitányi
D000465:Algorithms; D000818:Animals; D044962:Data Compression / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D006236:Handwriting; D006801:Humans; D051379:Mice; D008962:Models, Theoretical; D015388:Organelles / Q000378:metabolism; D010363:Pattern Recognition, Automated / Q000379:methods*; D012160:Retina / Q000166:cytology; D013234:Stem Cells / Q000166:cytology