高维金字塔匹配核改进算法

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。

Improved pyramid matching kernel for high dimension

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).E...