论文标题
HPCGEN：分层K-均值聚类和基于水平的扫描路径基因的主要成分
HPCGen: Hierarchical K-Means Clustering and Level Based Principal Components for Scan Path Genaration
论文作者
论文摘要
在本文中，我们提出了一种分解扫描路径及其生成新扫描路径的实用性的新方法。为此，我们将K-均值聚类过程用于原始目光数据，然后迭代地在发现的簇中找到更多簇。发现的群集在层次结构中的每个级别分组，最重要的主要组件是根据其中包含的数据计算的。使用此树层次结构和主要组件，可以生成与原始数据的人类行为相匹配的新扫描路径。我们表明，该生成的数据对于生成用于扫描路径分类的新数据非常有用，但也可以用于生成假扫描路径。
In this paper, we present a new approach for decomposing scan paths and its utility for generating new scan paths. For this purpose, we use the K-Means clustering procedure to the raw gaze data and subsequently iteratively to find more clusters in the found clusters. The found clusters are grouped for each level in the hierarchy, and the most important principal components are computed from the data contained in them. Using this tree hierarchy and the principal components, new scan paths can be generated that match the human behavior of the original data. We show that this generated data is very useful for generating new data for scan path classification but can also be used to generate fake scan paths.
