Spectral Graph Optimization for Instance Reduction
Spectral Graph Optimization for Instance Reduction
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DOI：
10.1109/tnnls.2012.2198832
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发表时间：
2012-05
影响因子：
10.4
通讯作者：
K. Nikolaidis;Eduardo Rodríguez-Martínez;J. Y. Goulermas;Qing-Hua Wu
中科院分区：
文献类型：
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作者：
K. Nikolaidis;Eduardo Rodríguez-Martínez;J. Y. Goulermas;Qing-Hua Wu
基于实例的学习算法的运行是基于在系统数据库中存储大量的原型集。然而，这样的系统通常会遇到存储需求、对噪声的敏感性和计算复杂性等问题，从而导致较高的搜索和响应时间。在本文中，我们介绍了一个新的框架，该框架利用谱图理论有效地将数据集划分为边界和内部实例。这是通过使用一组不同的边界区分特征来实现的，这些特征可以捕获样本的本地朋友和敌人概况。然后通过图切建模方法将这些特征融合的信息用于生成边界和非边界样本的最终数据集分区。所提出的方法被称为光谱实例约简（SIR）算法。大量数据集的实验表明，SIR在分类精度和数据浓缩两个目标上都比许多其他约简算法有竞争力。
The operation of instance-based learning algorithms is based on storing a large set of prototypes in the system's database. However, such systems often experience issues with storage requirements, sensitivity to noise, and computational complexity, which result in high search and response times. In this brief, we introduce a novel framework that employs spectral graph theory to efficiently partition the dataset to border and internal instances. This is achieved by using a diverse set of border-discriminating features that capture the local friend and enemy profiles of the samples. The fused information from these features is then used via graph-cut modeling approach to generate the final dataset partitions of border and nonborder samples. The proposed method is referred to as the spectral instance reduction (SIR) algorithm. Experiments with a large number of datasets show that SIR performs competitively compared to many other reduction algorithms, in terms of both objectives of classification accuracy and data condensation.