Multi-class semi-supervised kernel minimum squared error for face recognition

Abstract Kernel Minimum Squared Error (KMSE) has become a hot topic in machine learning and pattern recognition in the past years. However, KMSE is essentially a binary classifier and one-against-all and one-against-one strategies are usually employed to deal with multi-class problems. In this situation, KMSE needs to resolve multiple equations with the high computation complexity. Meanwhile, labeled examples are usually insufficient and unlabeled ones are abundant in many real-world applications. Therefore in this paper, we introduce a novel multi-class semi-supervised KMSE algorithm, called multi-class Laplacian regularized KMSE (McLapKMSE). Compared to KMSE and semi-supervised KMSE, we need resolve one equation at once and therefore the method has the lower complexity. The experiments on face recognition are conducted to illustrate that our algorithm can achieve the comparable performance and lower complexity in contrast to the other supervised and semi-supervised methods.