论文标题
颗粒计算：通过修改分区矩阵的增强脱粒方案
Granular Computing: An Augmented Scheme of Degranulation Through a Modified Partition Matrix
论文作者
论文摘要
作为人工智能的重要技术，粒状计算（GRC）已成为一种新的多学科范式，并且近年来受到了很多关注。形成大量数字数据的抽象和高效表征的信息颗粒已被视为GRC的基本结构。通过生成原型和分区矩阵，模糊聚类是一种通常遇到的信息颗粒方法。脱粒涉及根据颗粒代表完成的数据重建。先前的研究表明，重建误差与肉芽过程的性能之间存在关系。通常，脱粒误差越低，颗粒的性能越好。但是，现有的脱粒方法通常无法恢复原始数字数据，这是重建误差发生背后的重要原因之一。为了提高脱粒的质量，在这项研究中，我们通过修改分区矩阵来开发增强方案。通过提出增强方案，我们介绍了一系列新型的肉芽结构机制。在构造的方法中，原型可以表示为数据集矩阵和分区矩阵的乘积。然后，在脱粒过程中，可以将重建的数字数据分解为分区矩阵和原型矩阵的乘积。颗粒和脱粒都被认为是具有分区矩阵和模糊因子的数据子空间和原型子空间之间的广义旋转。通过修改分区矩阵，新的分区矩阵是通过一系列矩阵操作构建的。我们对开发计划进行了详尽的分析。实验结果与基本概念框架一致
As an important technology in artificial intelligence Granular Computing (GrC) has emerged as a new multi-disciplinary paradigm and received much attention in recent years. Information granules forming an abstract and efficient characterization of large volumes of numeric data have been considered as the fundamental constructs of GrC. By generating prototypes and partition matrix, fuzzy clustering is a commonly encountered way of information granulation. Degranulation involves data reconstruction completed on a basis of the granular representatives. Previous studies have shown that there is a relationship between the reconstruction error and the performance of the granulation process. Typically, the lower the degranulation error is, the better performance of granulation is. However, the existing methods of degranulation usually cannot restore the original numeric data, which is one of the important reasons behind the occurrence of the reconstruction error. To enhance the quality of degranulation, in this study, we develop an augmented scheme through modifying the partition matrix. By proposing the augmented scheme, we dwell on a novel collection of granulation-degranulation mechanisms. In the constructed approach, the prototypes can be expressed as the product of the dataset matrix and the partition matrix. Then, in the degranulation process, the reconstructed numeric data can be decomposed into the product of the partition matrix and the matrix of prototypes. Both the granulation and degranulation are regarded as generalized rotation between the data subspace and the prototype subspace with the partition matrix and the fuzzification factor. By modifying the partition matrix, the new partition matrix is constructed through a series of matrix operations. We offer a thorough analysis of the developed scheme. The experimental results are in agreement with the underlying conceptual framework
