A fast feature ranking algorithm for classification in the presence of high dimensionality and small sample size is proposed .The basic idea is that the important features force the data points of the same class to maintain their intrinsic neighbor relations ,whereas neighboring points of different classes are no longer to stick to one an-other .Applying this assumption ,an optimization problem weighting each feature is derived .The algorithm does not involve the dense matrix eigen-decomposition which can be computationally expensive in time .Extensive exper-iments are conducted to validate the significance of selected features using the Yale ,Extended YaleB and PIE data-sets .The thorough evaluation shows that ,using one-nearest neighbor classifier ,the recognition rates using 100-500 leading features selected by the algorithm distinctively outperform those with features selected by the baseline feature selection algorithms ,while using support vector machine features selected by the algorithm show less prominent improvement .Moreover ,the experiments demonstrate that the proposed algorithm is particularly effi-cient for multi-class face recognition problem .
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