Sparse Non-Gaussian Component Analysis

作者：Diederichs Elmar^*; Juditsky Anatoli; Spokoiny Vladimir; Schuette Christof

来源：IEEE Transactions on Information Theory, 2010, 56(6): 3033-3047.

DOI：10.1109/TIT.2010.2046229

• Convex projection
• model reduction
• principle component analysis (PCA)
• reduction of dimensionality
• sparsity
• structural adaptation
• variable selection

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 PCA-based procedure with a new the algorithm we refer to as convex projection.

• 出版日期2010-6