Streaming Sparse Principal Component Analysis
published: Sept. 27, 2015, recorded: July 2015, views: 1622
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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(slogp). 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.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !