Principal component analysis (PCA) is widely used in data processing and dimensionality reduction. However, PCA suffers from the fact that each principal component is a linear combination of all the original variables, thus it is often difficult to interpret the results. We introduce a new method called sparse principal component analysis (SPCA) using the lasso (elastic net) to produce modified...

Many tasks involving high-dimensional data, such as face recognition, suffer from the curse of dimensionality: the number of training samples required to accurately learn a classifier increases exponentially with the dimensionality of the data. Structured Principal Component Analysis (SPCA) reduces the dimensionality of the data by choosing a small number of features to represent larger sets of...

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. ...