Similarity and denoising

title = "Similarity and denoising",

journal = "Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences",

year = "2013",

volume = "371",

number = "1984",

keywords = "genetic algorithms, genetic programming, similarity, denoising, individual data, Kolmogorov complexity, information distance, lossy compression",

ISSN = "1364-503X",

URL = "

http://rsta.royalsocietypublishing.org/content/371/1984/20120091",

eprint = "http://rsta.royalsocietypublishing.org/content/371/1984/20120091.full.pdf",

DOI = "

doi:10.1098/rsta.2012.0091",

publisher = "The Royal Society",

size = "16 pages",

abstract = "We can discover the effective similarity among pairs of finite objects and denoise a finite object using the Kolmogorov complexity of these objects. The drawback is that the Kolmogorov complexity is not computable. If we approximate it, using a good real-world compressor, then it turns out that on natural data the processes give adequate results in practice. The methodology is parameter-free, alignment-free and works on individual data. We illustrate both methods with examples.",