INDEPENDENT COMPONENT ANALYSIS WITH JOINT SPEEDUP AND SUPERVISORY CONCEPT INJECTION: APPLICATIONS TO BRAIN fMRI MAP DISTILLATION

Methods to combine speedup terms and supervisory concept injection are presented. The speedup is based upon iterative optimization of the convex divergence. The injection of supervisory information is realized by adding a term which reduces an additional cost for a specified concept. Since the convex divergence includes usual logarithmic information measures, its direct application gives faster algorithms than existing logarithmic methods. This paper first shows a list of newly obtained general properties of the convex divergence. Then, these properties are used to derive faster algorithms for the independent component analysis. Then, an additional term for incorporating supervisory information is introduced. The efficiency of the total algorithm is tested using a set of real data brain fMRI time series. Successful results in view of convergence speed, software complexity, and extracted brain maps are reported. Finally, another class of the convex divergence optimization, the α-EM algorithm, is commented upon.