Boosting simple projections for multi-class dimensionality reduction

This paper presents a novel method for dimensionality reduction and for multi-class classification tasks. This method iteratively selects a series of simple but effective 1D subspaces, and then combines the corresponding 1D projections by Adaboost. M2. Its major advantages are: 1) it does not impose speci.c assumptions on data distribution; 2) it minimizes Bayes error estimation in low-dimensional space; 3) it simpli.es existing subspace-based methods to eigenvalue decomposition problem; and 4) each of the 1D subspaces (with associated nearest neighbor classi.er) has different emphasis - measured by weighted training error. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed method.