标题： 特征提取的运输模型 显示英文标题

标题： Transport Model for Feature Extraction

摘要： 我们提出了一种新的特征提取方法，用于复杂和大规模的数据集，该方法基于图上的传输算子概念。 所提出的方法将许多现有的基于扩散过程的数据表示方法推广和扩展到一个新的领域，在这个领域中动态系统起着关键作用。 这种方法的主要优势来自于能够利用不同于例如图拉普拉斯上下文中的不同关系。 证明了传输算子的基本性质。 通过引入多种不同的变换示例来展示该方法的灵活性。 我们通过一系列计算实验和对高光谱卫星图像分类问题的应用来结束本文，以说明我们算法的实际意义及其量化复杂数据集中新方面关系的能力。 显示英文摘要

摘要： We present a new feature extraction method for complex and large datasets, based on the concept of transport operators on graphs. The proposed approach generalizes and extends the many existing data representation methodologies built upon diffusion processes, to a new domain where dynamical systems play a key role. The main advantage of this approach comes from the ability to exploit different relationships than those arising in the context of e.g., Graph Laplacians. Fundamental properties of the transport operators are proved. We demonstrate the flexibility of the method by introducing several diverse examples of transformations. We close the paper with a series of computational experiments and applications to the problem of classification of hyperspectral satellite imagery, to illustrate the practical implications of our algorithm and its ability to quantify new aspects of relationships within complicated datasets.