Sparse Principal Component Analysis with Constraints
Mihajlo Grbovic, Chris Dance, Slobodan Vucetic
The sparse principal component analysis is a variant of
the classical principal component analysis, which finds
linear combinations of a small number of features that
maximize variance across data. In this paper we propose
a methodology for adding two general types of feature
grouping constraints into the original sparse PCA optimization
procedure.We derive convex relaxations of the
considered constraints, ensuring the convexity of the resulting
optimization problem. Empirical evaluation on
three real-world problems, one in process monitoring
sensor networks and two in social networks, serves to
illustrate the usefulness of the proposed methodology.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, Toronto, July 22–26, 2012.