This paper introduces and analyzes several feature extraction algorithms. These algorithms use linear or non-linear feature extraction methods to project high-dimensional objects into lower dimensional space, thus the complexity of the operations upon them, such as clustering, the nearest-neighbor search, visualization and etc can be reduced. The paper also presents some comparative experimental results of these algorithms and analyzes briefly their advantages or shortcomings.
