In recent years, relevance feedback has been widely used to improve the performance of content-based image retrieval. The way in which to select a subset of features from a large-scale feature pool and to construct a suitable dissimilarity measure are key steps in a relevance feedback system. Biased discriminant analysis has been proposed to select features during relevance feedback iterations. However, to solve the BDA, we often encounter the matrix singular problem. In this paper, we propose a kernel-based discriminant analysis, which can overcome the matrix singular problem. The new method is shown to outperform the traditional kernel BDA and constrained support vector machine based relevance feedback algorithms.