Cardinal sparse partial least square feature selection and its application in face recognition

Many modern computer vision systems combine high dimensional features and linear classifiers to achieve better classification accuracy. However, the excessively long features are often highly redundant; thus dramatically increases the system storage and computational load. This paper presents a novel feature selection algorithm, namely cardinal sparse partial least square algorithm, to address this deficiency in an effective way. The proposed algorithm is based on the sparse solution of partial least square regression. It aims to select a sufficiently large number of features, which can achieve good accuracy when used with linear classifiers. We applied the algorithm to a face recognition system and achieved the stateof- the-art results with significantly shorter feature vectors.