Independent component selection (ICS), introduced by Tyler et al. (2009, JRSS B), is a powerful tool to find potentially interesting projections of multivariate data. In some cases, some of the projections proposed by ICS come close to really interesting ones, but little deviations can result in a blurred view which does not reveal the feature (e.g. a clustering) which would otherwise be clearly visible. To remedy this problem, we propose an automated and localized version of projection pursuit (PP), cf. Huber (1985, Ann. Statist.}. Precisely, our local search is based on gradient descent applied to estimated differential entropy as a function of the projection matrix.

翻译：Tyler等人(2009年,JRSS B)提出的独立组成部分选择(ICS)是找到对多种变式数据的潜在有趣预测的有力工具,在某些情况下,ICS提出的一些预测接近于真正有趣的预测,但几乎没有偏差会导致一种模糊的视角,无法揭示否则会明显可见的特征(如集群),为解决这一问题,我们建议采用自动和本地化的投影追踪版本(PPP),参考Huber(1985年,Ann. Statist.}。