Streaming Sparse Principal Component Analysis
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:494-503, 2015.
This paper considers estimating the leading k principal components with at most s non-zero attributes from p-dimensional samples collected sequentially in memory limited environments. We develop and analyze two memory and computational efficient algorithms called streaming sparse PCA and streaming sparse ECA for analyzing data generated according to the spike model and the elliptical model respectively. In particular, the proposed algorithms have memory complexity O(pk), computational complexity O(pk mink,slogp) and sample complexity Θ(s \log p). We provide their finite sample performance guarantees, which implies statistical consistency in the high dimensional regime. Numerical experiments on synthetic and real-world datasets demonstrate good empirical performance of the proposed algorithms.