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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Simultaneous Global and Local Graph Structure Preserving for Multiple Kernel Clustering
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Simultaneous Global and Local Graph Structure Preserving for Multiple Kernel Clustering

机译：多个内核聚类的同步全局和本地图形结构

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摘要

Multiple kernel learning (MKL) is generally recognized to perform better than single kernel learning (SKL) in handling nonlinear clustering problem, largely thanks to MKL avoids selecting and tuning predefined kernel. By integrating the self-expression learning framework, the graph-based MKL subspace clustering has recently attracted considerable attention. However, the graph structure of data in kernel space is largely ignored by previous MKL methods, which is a key concept of affinity graph construction for spectral clustering purposes. In order to address this problem, a novel MKL method is proposed in this article, namely, structure-preserving multiple kernel clustering (SPMKC). Specifically, SPMKC proposes a new kernel affine weight strategy to learn an optimal consensus kernel from a predefined kernel pool, which can assign a suitable weight for each base kernel automatically. Furthermore, SPMKC proposes a kernel group self-expressiveness term and a kernel adaptive local structure learning term to preserve the global and local structure of the input data in kernel space, respectively, rather than the original space. In addition, an efficient algorithm is proposed to solve the resulting unified objective function, which iteratively updates the consensus kernel and the affinity graph so that collaboratively promoting each of them to reach the optimum condition. Experiments on both image and text clustering demonstrate that SPMKC outperforms the state-of-the-art MKL clustering methods in terms of clustering performance and computational cost.
机译：多个内核学习（MKL）通常被识别到在处理非线性聚类问题方面比单个内核学习（SKL）更好地执行，这主要是由于MKL避免选择和调整预定义内核。通过集成自我表达学习框架，基于图形的MKL子空间群集最近引起了相当大的关注。然而，内核空间中的数据的图形结构主要被先前的MKL方法忽略，这是用于光谱聚类目的的亲和图构造的关键概念。为了解决这个问题，在本文中提出了一种新的MKL方法，即结构保留多个内核聚类（SPMKC）。具体而言，SPMKC提出了一种新的内核仿射权重策略，用于从预定义的内核池中学习最佳共识内核，其可以自动为每个基础内核分配合适的权重。此外，SPMKC提出了内核组自我表达式术语和内核自适应本地结构学习术语，以分别保留内核空间中输入数据的全局和本地结构，而不是原始空间。另外，提出了一种有效的算法来解决所得到的统一目标函数，其迭代地更新共识内核和亲和图，使得协同促进它们中的每一个以达到最佳状态。图像和文本聚类的实验证明了SPMKC在聚类性能和计算成本方面优于最先进的MKL聚类方法。

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