Universal behavior in largescale aggregation of independent noisy observations
Abstract
Aggregation of noisy observations involves a difficult tradeoff between observation quality, which can be increased by increasing the number of observations, and aggregation quality which decreases if the number of observations is too large. We clarify this behavior for a prototypical system in which arbitrarily large numbers of observations exceeding the system capacity can be aggregated using lossy data compression. We show the existence of a scaling relation between the collective error and the system capacity, and show that largescale lossy aggregation can outperform lossless aggregation above a critical level of observation noise. Further, we show that universal results for scaling and critical value of noise can be obtained when the system capacity increases toward infinity.
 Publication:

EPL (Europhysics Letters)
 Pub Date:
 August 2009
 DOI:
 10.1209/02955075/87/48003
 arXiv:
 arXiv:0812.2726
 Bibcode:
 2009EL.....8748003M
 Keywords:

 Computer Science  Information Theory
 EPrint:
 10 pages, 3 figures