用于目标识别的最近素单纯复形
Nearest Prime Simplicial Complex for Object Recognition
摘要：
数据分布的结构表示对于理解数据生成的底层机制起着重要作用。在本文中，我们提出了最近的素数单纯复形方法（NSC），利用持久同源捕捉这样的结构。假设每个类都用一个素单纯复形表示，我们根据样本到单纯复形的最近投影距离对未标记样本进行分类。我们还扩展了这些复杂的投影约束项的外推能力。在模拟数据集和实际数据集上的实验表明，与几种已发表的算法相比，所提出的NSC方法在不损失结构表示的情况下取得了令人满意的性能。
英文摘要：
The structure representation of data distribution plays an important role in understanding the underlying mechanism of generating data. In this paper, we propose nearest prime simplicial complex approaches (NSC) by utilizing persistent homology to capture such structures. Assuming that each class is represented with a prime simplicial complex, we classify unlabeled samples based on the nearest projection distances from the samples to the simplicial complexes. We also extend the extrapolation ability of these complexes with a projection constraint term. Experiments in simulated and practical datasets indicate that compared with several published algorithms, the proposed NSC approaches achieve promising performance without losing the structure representation.
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