Semisupervised local preserving embedding algorithm based on maximum margin criterion for large‐scale data streams

Abstract

SummaryIn the field of machine learning, feature extraction is one of the most important preprocessing in data classification for its effectiveness, and now it has attracted much extensive attention for large‐scale data stream preprocessing step, especially in the era of big data. Motivated by the advantages of unsupervised and supervised feature extraction, which are two desirable and promising characteristics for dimension reduction, a new semisupervised local preserving embedding algorithm based on maximum margin criterion (SLPE/MMC) is proposed in this paper. First, the objective functions of maximum margin criterion (MMC) and neighborhood preserving embedding (NPE) are combined to get the first objective function of SLPE/MMC. Then, in order to overcome the out‐of‐sample problem, a linear transformation is introduced to construct the second objective function. At last, the whole optimal objective function is constructed by combing the two objective functions together. The proposed algorithm has effectively taken advantage of the sample's supervised information and keeps the geometry structure and the class discrimination information of the manifold. Experiments on face datasets Yale, CMU PIE, and AR datasets are performed to evaluate the classification accuracy of SLPE/MMC. The experimental results and time complexity comparisons have demonstrated the effectiveness of the proposed method.