Merging Subspace Models for Face Recognition
Merging Subspace Models for Face Recognition
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DOI：
10.1007/978-3-540-45179-2_74
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发表时间：
2003-08
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影响因子：
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通讯作者：
W. Skarbek
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文献类型：
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作者：
W. Skarbek
考虑主子空间模型的合并问题：给定两个独立训练数据序列的主子空间模型，假设原始数据不可用，找到原始数据集并集的子空间模型。提出了主子空间归并(PSM)算法及其近似形式(APSM)来解决该问题。对该方法的准确性和复杂性进行了数学分析，并在人脸图像模型上进行了验证。如果将数据向量投影到维度N维特征空间的线性子空间，则该算法的时间复杂度为O(r(4n2+13r2))。
The merging problem for principal subspace (PS) models is considered in the form: given two principal subspace modelsfor independent training data sequences, assuming that the original data is not available, find the subspace model for the union of the original data sets. The principal subspace merging (PSM) algorithm and its approximated version (APSM) are proposed to solve the problem. The accuracy and the complexity of the approach has been mathematically analyzed and verified on face image models. If data vectors are modeled by projections into a linear subspace of dimensionrinNdimensional feature space then the algorithm hasO(r(4N2+13r2)) time complexity.