The analysis of high dimensional data is encountered in many areas. In the analysis of that kind of data, dimensionality reduction methods are used to work with fewer dimensions. Generally, linear dimensionality reduction methods are more preferred. In this paper, popular linear dimensionality reduction methods and their performance are investigated. These methods are principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE) and locality sensitive discriminant analysis (LSDA).