论文标题
通过最近约束的子空间分类器进行固有维度估计
Intrinsic Dimension Estimation via Nearest Constrained Subspace Classifier
论文作者
论文摘要
我们考虑了图像数据的分类问题和内在维度估计。提出了一个新的基于子空间的分类器，以进行监督分类或内在维度估计。每个类中的数据的分布由特征空间的有限数量的咖啡因子空间的结合进行建模。仿射子空间具有一个共同的维度，假定该尺寸远小于特征空间的维度。使用基于L0-norm的回归发现子空间。提出的方法是经典NN（最近的邻居），NFL（最近的特征线）分类器的概括，并且与NS（最近的子空间）分类器有着密切的关系。具有准确估计的维度参数的拟议分类器通常在分类准确性方面优于其竞争对手。我们还使用邻里表示形式提出了一个快速版本的分类器，以降低其计算复杂性。公开可用数据集的实验证实了这些主张。
We consider the problems of classification and intrinsic dimension estimation on image data. A new subspace based classifier is proposed for supervised classification or intrinsic dimension estimation. The distribution of the data in each class is modeled by a union of of a finite number ofaffine subspaces of the feature space. The affine subspaces have a common dimension, which is assumed to be much less than the dimension of the feature space. The subspaces are found using regression based on the L0-norm. The proposed method is a generalisation of classical NN (Nearest Neighbor), NFL (Nearest Feature Line) classifiers and has a close relationship to NS (Nearest Subspace) classifier. The proposed classifier with an accurately estimated dimension parameter generally outperforms its competitors in terms of classification accuracy. We also propose a fast version of the classifier using a neighborhood representation to reduce its computational complexity. Experiments on publicly available datasets corroborate these claims.
