
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.
Signal theory (characterization, reconstruction, filtering, etc.), Taxonomy, cladistics, statistics in mathematical biology, 025, 004
Signal theory (characterization, reconstruction, filtering, etc.), Taxonomy, cladistics, statistics in mathematical biology, 025, 004
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 13 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
