On utilizing search methods to select subspace dimensions for kernel-based nonlinear subspace classifiers
On utilizing search methods to select subspace dimensions for kernel-based nonlinear subspace classifiers
复制标题
利用搜索方法为基于核的非线性子空间分类器选择子空间维度
DOI：
10.1109/tpami.2005.15
复制
发表时间：
2005
影响因子：
23.6
通讯作者：
B. Oommen
中科院分区：
文献类型：
--
作者：
Sang;B. Oommen
在基于核的非线性子空间（KNS）方法中，子空间维数对子空间分类器的性能有很大的影响。为了获得高的分类精度，通常需要较大的维数。然而，如果选择的子空间维度太大，则由于所得子空间的重叠而导致低性能，并且如果选择的子空间维度太小，则由于所得近似差而增加分类误差。最常见的方法是一种特别的性质，它根据从每个类的核矩阵计算出的所谓累积比例来选择维度。我们提出了一种新的方法，系统和有效地选择最佳或接近最佳的子空间维度的KNS分类器使用的搜索策略和启发式函数称为重叠标准。这一职能的理由已在本文件正文中说明。选择最佳子空间维度的任务被简化为使用该标准作为启发式函数从给定的问题域解决方案空间中找到最佳子空间维度。因此，可以修剪搜索空间以非常有效地找到最佳解决方案。我们的实验结果表明，所提出的机制有效地选择维度，而不牺牲分类精度。
In kernel-based nonlinear subspace (KNS) methods, the subspace dimensions have a strong influence on the performance of the subspace classifier. In order to get a high classification accuracy, a large dimension is generally required. However, if the chosen subspace dimension is too large, it leads to a low performance due to the overlapping of the resultant subspaces and, if it is too small, it increases the classification error due to the poor resulting approximation. The most common approach is of an ad hoc nature, which selects the dimensions based on the so-called cumulative proportion computed from the kernel matrix for each class. We propose a new method of systematically and efficiently selecting optimal or near-optimal subspace dimensions for KNS classifiers using a search strategy and a heuristic function termed the overlapping criterion. The rationale for this function has been motivated in the body of the paper. The task of selecting optimal subspace dimensions is reduced to find the best ones from a given problem-domain solution space using this criterion as a heuristic function. Thus, the search space can be pruned to very efficiently find the best solution. Our experimental results demonstrate that the proposed mechanism selects the dimensions efficiently without sacrificing the classification accuracy.