基于正则化秩k矩阵逼近的稀疏主成分分析

Sparse principal component analysis via regularized rank-k matrix approximation

在计算稀疏主成分(PCs)时,由于同时求k个主成分的做法可以减少计算所产生的累积误差,因此提出了基于正则化秩k矩阵逼近的稀疏主成分模型,并设计了求解该模型的块坐标下降法(BCD-s PCA-r SVD)。该算法的主要思想是先把变量按坐标分成2k个块,当固定其他2k-1个坐标块的变量时,求解关于单个坐标块的子问题并给出子问题的显式解,循环地求解这些子问题直至满足终止条件。该算法每次迭代的计算复杂度关于样本个数与变量维数都是线性的,并且证明了它是收敛的。该算法不仅易于实现,数值仿真结果表明,该算法应用到真实数据与合成数据上都是可行且有效的。它不仅使累积误差降低,而且具有较低的计算复杂度,因而可以有效地求解大规模稀疏主成分分析问题。

In calculating the sparse principal components （PCs） , attaining k PCs simultaneously can re- duce the accumulated error arising from the calculation process. We proposed the sparse principal component model via regularized rank-k matrix approximation and designed a block coordinate descent method （BCD- sPCA-rSVD） to solve this problem. Its main idea is to first divide variables into 2k blocks by coordinates, and then solve sub-problem with respect to each single coordinate block when keeping other 2k - 1 variables fixed. By solving these sub-problems with explicit solutions reeursively until the stopping criterion is satisfied, the BCD-sPCA-rSVD algorithm can be easily constructed. Its per-iteration complexity is linear in both sample size and variable dimensionality. The algorithm is convergent and easy to implement. Numerical simulation results show that the algorithm is feasible and effective when applied to real and synthetic data sets. The proposed method reduces the accumulated error and has lower computational complexity, which makes it well suited to handling large-scale problems of sparse principal component analysis.