论文标题
最小距离测量的记忆有效抽样
Memory-Efficient Sampling for Minimax Distance Measures
论文作者
论文摘要
Minimax距离度量以无监督的方式提取基础模式和歧管。现有方法需要相对于对象数量的二次内存。在本文中，我们研究了有效的采样方案，以减少记忆要求并提供线性空间复杂性。特别是，我们提出了一种新型的采样技术，可很好地适应最小距离。我们评估来自不同域中现实世界数据集的方法，并分析结果。
Minimax distance measure extracts the underlying patterns and manifolds in an unsupervised manner. The existing methods require a quadratic memory with respect to the number of objects. In this paper, we investigate efficient sampling schemes in order to reduce the memory requirement and provide a linear space complexity. In particular, we propose a novel sampling technique that adapts well with Minimax distances. We evaluate the methods on real-world datasets from different domains and analyze the results.
