Dynamics and Performance of Susceptibility Propagation on Synthetic Data
Wo study the performance and convergence properties of the Susceptibility Propagation (SusP) algorithm for solving the Inverse Ising problem. We first study how the temperature parameter (T) in a SherringtonKirkpatrick model generating the data influences the performance and convergence of the algorithm. We find that at the high temperature regime (T > 4). the algorithm performs well and its quality is only limited by the quality of the. supplied data. In the low temperature regime (T < 4), we find that the algorithm typically does not converge, yielding diverging values for the couplings. However, we show that by stopping the algorithm at the right time before divergence becomes serious, good reconstruction can be achieved down to T ≈ 2. We then show that dense connectivity, loopiness of the connectivity, and high absolute magnetization all have deteriorating effects on the performance of the algorithm. When absolute magnetization is high, we show that other methods can be work better than SusP Finally, we show that for neural data with high absolute magnetization, SusP performs less well than TAP inversion.
spin-glass and other random models inference methods computational methods in statistical physics and nonlinear dynamics
Erik Aurell Charles Ollion Yasser Roudi
ACCESS Linnaeus Center KTH-Royal Institute of Technology. 100 44 Stockholm, Sweden Department of Inf ACCESS Linnaeus Center KTH-Royal Institute of Technology. 10044 Stockholm, Sweden Department of Comp NORDITA, Roslagstullsbackcn 23, 10691 Stockholm. Sweden
国际会议
International Workshop on Statistical Physics and Computer Sciences(统计物理与计算机科学交叉研究国际研讨会 )
北京
英文
16-24
2010-07-08（万方平台首次上网日期，不代表论文的发表时间）