[hal-00381120, v1] Sparse NonGaussian Component Analysis

Non-gaussian component analysis (NGCA) introduced in [24] offered a method for high dimensional data analysis allowing for identifying a low-dimensional non-Gaussian component of the whole distribution in an iterative and structure adaptive way. An important step of the NGCA procedure is identification of the non-Gaussian subspace using Principle Component Analysis (PCA) method. This article proposes a new approach to NGCA called sparse NGCA which replaces the PCAbased procedure with a new the algorithm we refer to as convex projection. keywords: reduction of dimensionality, model reduction, sparsity, variable selection, principle component analysis, structural adaptation, convex projection Mathematical Subject Classification: 62G05, 60G10, 60G35, 62M10, 93E10 Supported by DFG research center Matheon ”Mathematics for key technologies” (FZT 86) in Berlin. ha l-0 03 81 12 0, v er si on 1 5 M ay 2 00 9