Phase Transition in a Noise Reduction Model: Shrinking or Percolation?

dc.creatorvan Mourik, J.
dc.creatorWong, K. Y. Michael
dc.creatorBolle', D.
dc.date1998-04-09
dc.date.accessioned2026-07-07T03:10:18Z
dc.date.available2026-07-07T03:10:18Z
dc.descriptionA model of noise reduction (NR) for signal processing is introduced. Each noise source puts a symmetric constraint on the space of the signal vector within a tolerable overlap. When the number of noise sources increases, sequences of transitions take place, causing the solution space to vanish. We found that the transition from an extended solution space to a shrunk space is retarded because of the symmetry of the constraints, in contrast to the analogous problem of pattern storage. For low tolerance, the solution space vanishes by volume reduction, whereas for high tolerance, the vanishing becomes more and more like percolation. The model is studied in the replica symmetric, first step and full replica symmetry breaking schemes.
dc.description4 pages, 2 figures
dc.identifierhttps://arxiv.org/abs/cond-mat/9804115
dc.identifierhttp://arxiv.org/abs/cond-mat/9804115
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/28199
dc.subjectDisordered Systems and Neural Networks
dc.subjectStatistical Mechanics
dc.titlePhase Transition in a Noise Reduction Model: Shrinking or Percolation?
dc.typetext

Files

Collections