IVFS：简单有效的高维拓扑保持特征选择
IVFS: Simple and Efficient Feature Selection for High Dimensional Topology Preservation
摘要：
特征选择是处理高维数据的重要工具。在无监督的情况下，许多流行的算法旨在保持原始数据的结构。在本文中，我们提出了一个简单而有效的特征选择算法，以提高样本的相似性保护，通过一个新的角度，拓扑结构的保护，这是由持久的图表从计算拓扑的上下文中表示。该方法是在一个统一的特征选择框架IVFS上设计的，该框架是受随机子集方法的启发而设计的。该计划是灵活的，可以处理的情况下，问题是分析棘手的。该算法能够很好地保持完整数据的两两距离以及拓扑模式。我们证明，我们的算法可以提供令人满意的性能下，尖锐的子采样率，这支持我们提出的方法有效地实现大规模数据集。大量的实验验证了所提出的特征选择方案的有效性。
英文摘要：
Feature selection is an important tool to deal with high dimensional data. In unsupervised case, many popular algorithms aim at maintaining the structure of the original data. In this paper, we propose a simple and effective feature selection algorithm to enhance sample similarity preservation through a new perspective, topology preservation, which is represented by persistent diagrams from the context of computational topology. This method is designed upon a unified feature selection framework called IVFS, which is inspired by random subset method. The scheme is flexible and can handle cases where the problem is analytically intractable. The proposed algorithm is able to well preserve the pairwise distances, as well as topological patterns, of the full data. We demonstrate that our algorithm can provide satisfactory performance under a sharp sub-sampling rate, which supports efficient implementation of our proposed method to large scale datasets. Extensive experiments validate the effectiveness of the proposed feature selection scheme.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
