This document discusses dimensionality reduction algorithms and proposes a unified framework. It summarizes previous algorithms like PCA, LDA, ISOMAP and introduces a general formulation based on graph embedding. This includes direct graph embedding, linearization, kernelization and tensorization. It then presents a new algorithm called Marginal Fisher Analysis and experiments showing its advantage in face recognition. Finally it acknowledges contributions from other researchers in related areas.