A Fast deflation Method for Sparse Principal Component Analysis via Subspace Projections
This work addresses computational efficiency for researchers and practitioners using SPCA on high-dimensional data, representing an incremental improvement over existing methods.
The paper tackles the high computational cost of sparse principal component analysis (SPCA) on high-dimensional data by developing a fast deflation method called SPCA-SP using subspace projections via Household QR factorization, achieving a good tradeoff between sparsity, orthogonality, explained variance, balance of sparsity, and computational cost, with experiments confirming its effectiveness on benchmark datasets.
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.