In order to deal with the classification problem for high-dimensional and small-sized data

a kind of support vector machine based on random subspace and orthogonal locality preserving projection was proposed.The random subspace method was used to select a feature subset from the original feature space randomly for several times.Based on the selected feature subset

several base support vector machine (SVM) classifiers were generated.The orthogonal locality preserving projection method was adopted to carry out feature extraction on the samples of each base classifiers

which can

effectively

realize dimensionality reduction.We applied the processed samples to train each base classifiers.The results of the base SVM classifiers were integrated to obtain the final classification result

using a bayesian sum rule.Results on two publicly available face databases show the feasibility and validity of our proposed method.