Transport Model for Feature Extraction
Transport Model for Feature Extraction
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DOI：
10.1137/19m1296926
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
W. Czaja;Dong Dong-Dong;P. Jabin;Franck Olivier Ndjakou Njeunje
中科院分区：
文献类型：
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作者：
W. Czaja;Dong Dong-Dong;P. Jabin;Franck Olivier Ndjakou Njeunje
我们基于图上传输算子的概念，提出了一种针对复杂和大型数据集的新特征提取方法。所提出的方法概括并扩展了基于扩散过程的许多现有数据表示方法，到动力系统发挥关键作用的新领域。这种方法的主要优点来自于利用与图拉普拉斯等上下文中出现的关系不同的关系的能力。运输运营商的基本属性得到了证明。我们通过介绍几个不同的转换示例来证明该方法的灵活性。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，以说明我们的算法的实际含义及其量化复杂数据集中关系的新方面的能力。
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.