Structured Sparse Principal Component Analysis

Principal component analysis (PCA) is an essential tool for data analysis and unsupervised dimensionality reduction, whose goal is to find, among linear combinations of the data variables, a sequence of orthogonal factors that most efficiently explain the variance of the observations. One of its main shortcomings is that, even if PCA finds a small number of important factors, the factor themselves typically involve all original variables. In the last decade, several alternatives to PCA which find sparse and potentially interpretable factors have been proposed, notably nonnegative matrix factorization (NMF) [2] and sparse PCA (SPCA) [3, 4, 5]. However, in many applications, only constraining the size of the factors does not seem appropriate because the considered factors are not only expected to be sparse but also to have a certain structure. In fact, the popularity of NMF for face image analysis owes essentially to the fact that the method happens to retrieve sets of variables that are localized on the face and capture some features or parts of the face which seem intuitively meaningful given our a priori.