Faculty of Electrical Engineering, Warsaw University of Technology, Poland.
Neural Netw. 2021 Aug;140:247-260. doi: 10.1016/j.neunet.2021.03.018. Epub 2021 Mar 24.
We introduce a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). We maintain the advantages of the conventional NeRV method, while proposing an improvement of the data samples' neighborhood width calculation, in the input and output data space. In the standard NeRV, the data samples' neighborhood widths are determined in an arbitrary manner, in this way, inhibiting the possible quality of the resulting data visualization. We propose to compute the widths adaptively, on the basis of the input data scattering. Therefore, we first perform the preliminary input data clustering, next, we calculate the values of the inner-cluster variances, which convey the information on the input data scattering, then, we assign them to each data sample, and finally, we use them as the basis for the data samples' neighborhood widths determination. The results of the experiments conducted on the three different real datasets confirm the effectiveness and usefulness of the proposed approach.
我们介绍了一种新颖的自适应邻域检索可视化（NeRV）方法。我们保留了传统 NeRV 方法的优点，同时对输入和输出数据空间中的数据样本邻域宽度计算进行了改进。在标准 NeRV 中，数据样本的邻域宽度是任意确定的，这样会抑制可能得到的数据可视化的质量。我们建议根据输入数据的分散情况自适应地计算宽度。因此，我们首先对输入数据进行初步聚类，然后计算内聚类方差值，这些值传达了输入数据分散的信息，然后将它们分配给每个数据样本，最后使用它们作为确定数据样本邻域宽度的基础。在三个不同的真实数据集上进行的实验结果证实了所提出方法的有效性和实用性。
