The sparse principal component analysis is a variant of the classical analysis, which finds linear combinations small number features that maximize variance across data. In this paper we propose methodology for adding two general types feature grouping constraints into original PCA optimization procedure.We derive convex relaxations considered constraints, ensuring convexity resulting problem. ...

Online robust principal component analysis (RPCA) algorithms recursively decompose incoming data into low-rank and sparse components. However, they operate on vectors cannot directly be applied to higher-order arrays (e.g. video frames). In this paper, we propose a new online PCA algorithm that preserves the multi-dimensional structure of data. Our is based recently proposed tensor singular val...