随着特征维数增加，原金字塔匹配核（PMK）期望误差线性上升，从而性能存在着大幅下降的可能。提出一种改进的金字塔匹配算法，通过不断的二分维特征空间从而产生一系列特征子空间，加权求和每一特征子空间内对特征的金字塔匹配核，最后通过核优化得到半正定核矩阵，从而能够利用基于核学习算法（如支持向量机）求解。在两个数据集（Caltech-101、ETH-80）上的实验表明，相对于其他相应改进算法需要增加几百倍的计算时间，DP-PMK只增加46倍的计算时间就能够达到与其一样的准确率。

As the feature dimension increases，the original PMK suffers from distortion factors that increase linearly with the feature dimension.This paper proposes a new method by consistently dividing the feature space into two subspaces while generating several levels.In each subspace of the level，the original pyramid matching is used.Then a weighted sum of every subspace at each level is made.To optimize the added kernel matrix，we get a p.s.d.kernel which can be used in kernel based learning methods (such as SVM).Experiments on dataset Caltech-101 and ETH-80 show that：compared with other related algorithms which need hundreds of times of original computational time，It takes only about 46 times of original computational time to obtain the same accuracy by using the method of DP-PMK.