SPM Article

Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. A related easier problem is termed subspace learning or subspace estimation. Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning (RSL) or robust PCA (RPCA).

Individual feature articles and special issues are two major mechanisms of full-length tutorial surveys of IEEE Signal Processing Magazine (SPM). Since the May 2016 feature article cluster by Jane Wang et al. on brain signal analytics, SPM’s current and past editors-in-chief and their teams have been exploring a different way to complement this existing structure - a feature article cluster (or mini special issue) that allows for a set of three to five solicited articles on a current topic, instead of just one (feature article) or ten to 11 (a special issue).

I love the last scene from the movie Planet of the Apes (1968), which revealed how the human race destroyed its beautiful planet only to be overtaken by intelligent apes. In that movie, humans were the victims of their own intelligence.
 

I just got back from the 2018 International Conference on Acoustics, Speech, and Signal Processing in Calgary, Alberta, Canada. One of the highlights for me as editor-in-chief (EIC) of IEEE Signal Processing Magazine (SPM) is SPM’s editorial board meeting.

Discrete-time rational transfer functions are often converted to parallel second-order sections due to better numerical performance compared to direct form infinite impulse response (IIR) implementations. This is usually done by performing partial fraction expansion over the original transfer function.