LAAT：发现多个密度变化的微弱低维结构的局部排列蚂蚁技术
LAAT: Locally Aligned Ant Technique for discovering multiple faint low dimensional structures of varying density
摘要：
简化和聚类通常被用作许多复杂机器学习任务的预备步骤。噪声和异常值的存在会降低这种预处理的性能，从而极大地损害后续分析。在流形学习中，一些研究表明，当密度大大高于噪声所表现出的密度时，可以去除背景噪声或靠近结构的噪声。然而，在许多应用中，包括天文数据集，密度随着隐藏在噪声背景中的流形而变化。基于蚁群算法的思想，提出了一种新的噪声背景下的流形提取方法。与现有的随机游走解决方案相比，我们的技术捕获了与流形的主要方向局部对齐的点。此外，我们的经验表明，蚂蚁信息素的生物启发配方加强了这种行为，使其能够恢复嵌入在非常嘈杂的数据云中的多个流形。在几个合成和真实的数据集，包括一个N-体模拟的宇宙体积的算法性能相比，国家的最先进的方法，用于降低噪声的歧管检测和聚类证明。
英文摘要：
Dimensionality reduction and clustering are often used as preliminary steps for many complex machine learning tasks. The presence of noise and outliers can deteriorate the performance of such preprocessing and therefore impair the subsequent analysis tremendously. In manifold learning, several studies indicate solutions for removing background noise or noise close to the structure when the density is substantially higher than that exhibited by the noise. However, in many applications, including astronomical datasets, the density varies alongside manifolds that are buried in a noisy background. We propose a novel method to extract manifolds in the presence of noise based on the idea of Ant colony optimization. In contrast to the existing random walk solutions, our technique captures points that are locally aligned with major directions of the manifold. Moreover, we empirically show that the biologically inspired formulation of ant pheromone reinforces this behavior enabling it to recover multiple manifolds embedded in extremely noisy data clouds. The algorithm performance in comparison to state-of-the-art approaches for noise reduction in manifold detection and clustering is demonstrated, on several synthetic and real datasets, including an N-body simulation of a cosmological volume.
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。
