Communication-efficient Distributed Sparse Linear Discriminant Analysis

High dimensionality is a frequently confronted problem in many applications of machine learning. It increases time and space requirements for processing the data. Moreover, many machine learning methods tend to over-fit and become less interpretable in the presence of many irrelevant or redundant features. A common way to address this problem is the dimensionality reduction. Principal Component Analysis (PCA) (Jolliffe, 2002) is probably the most widely used dimensionality reduction method. However, it is an unsupervised dimensionality reduction method and does not consider the labels of the data. In order to take the label information into account, supervised dimensionality reduction methods are favored. Linear Discriminant Analysis (LDA) (Anderson, 1968), which is initially proposed as a classification method, is an important supervised dimensionality reduction method.