  The implementation of conventional sparse principal component analysis (SPCA)
on high-dimensional data sets has become a time consuming work. In this paper,
a series of subspace projections are constructed efficiently by using Household
QR factorization. With the aid of these subspace projections, a fast deflation
method, called SPCA-SP, is developed for SPCA. This method keeps a good
tradeoff between various criteria, including sparsity, orthogonality, explained
variance, balance of sparsity, and computational cost. Comparative experiments
on the benchmark data sets confirm the effectiveness of the proposed method.
