Sparse principal component analysis for feature selection of multiple physiological signals from flight task

Sparse principal component analysis (SPCA) imposes extra constraints or penalty terms to the standard PCA to achieve sparsity. In this paper, we introduce an efficient algorithm for finding an effective sparse feature principal component (PC) of multiple physiological signals. The algorithm consists of two stages. In the first stage, it identifies an active index set with a desired cardinality corresponding to the nonzero entries of the PC. In the second one, it uses the power iteration method to find the best direction with respect to the active index set. Experiments on randomly generated data and multiple physiological signals datasets show that our algorithm is very fast, especially on large and sparse data sets, while the numerical quality of the solution is comparable to the state-of-art algorithm.