Sparse Principal Component Analysis (SPCA)
Laurent El Ghaoui and Michael Jordan
In this work, we seek to decompose a matrix into sparse principal components. This has applications, for example, in genomics, allowing us to identify a few genes that explain most of the variance observed in micro-array data. We investigate various algorithms based on convex relaxations, their complexity, and the quality of the corresponding approximations.