Fuzzy local discriminant embedding for image feature extraction

In pattern recognition, feature extraction techniques have been widely employed to reduce the high dimensionality of data. In this paper, we propose a novel algorithm called fuzzy local discriminant embedding (FLDE) based on the unsupervised discriminant projection criterion and fuzzy set theory for image feature extraction and recognition. In the proposed method, a membership degree matrix is firstly calculated using the fuzzy k-nearest neighbor (FKNN) algorithm, and then the membership degree and the label information are incorporated into the definition of the weighted matrices to get the fuzzy local scatter and fuzzy nonlocal scatter. After characterizing the fuzzy nonlocal scatter and the fuzzy local scatter, a concise feature extraction criterion is derived via maximizing the ratio between them. Experimental results on the ORL, FERET, and CMU PIE face databases show the effectiveness of the proposed method.