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首页> 外文期刊>Optimization: A Journal of Mathematical Programming and Operations Research >A splitting method for the locality regularized semi-supervised subspace clustering
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A splitting method for the locality regularized semi-supervised subspace clustering

机译：用于局部正常化半监控子空间群集的分裂方法

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摘要

Graph-based semi-supervised learning (G-SSL) methods play an increasingly important role in machine learning systems. Recently, latent low-rank representation (LatLRR) graph has gained great success in subspace clustering. However, LatLRR only considers the global structure, while the local geometric information, which is often important to many real applications, is ignored. In this paper, we propose a locality regularized LatLRR model (LR-LatLRR) for semi-supervised subspace clustering problems. This model incorporates two regularization terms into LatLRR by taking the local structure of data into account. Then, we develop an efficient splitting algorithm for solving LR-LatLRR. In addition, we also prove the global convergence of the proposed algorithm. Furthermore, we extend the LR-LatLRR model to a case of including the non-negative constraint. Finally, we conduct experiments on a synthetic data and several real data sets for the semi-supervised clustering problems. Experimental results show that our method can obtain high classification accuracy and outperforms several state-of-the-art G-SSL methods.
机译：基于图形的半监督学习（G-SSL）方法在机器学习系统中发挥着越来越重要的作用。最近，潜在的低级表示（Latlrr）图已经在子空间聚类中获得了巨大的成功。但是，Latlrr仅考虑全局结构，而忽略了许多真实应用的本地几何信息是忽略的。在本文中，我们提出了一个用于半监控子空间聚类问题的地方正规化的LATLRR模型（LR-LATLRR）。该模型通过将数据的本地结构考虑到帐户，将两个正则化术语包含在Latlrr中。然后，我们开发一种用于解决LR-Latlrr的高效分离算法。此外，我们还证明了该算法的全局融合。此外，我们将LR-Latlrr模型扩展到包括非负约束的情况。最后，我们对Synthetic Data进行实验和用于半监督聚类问题的几个真实数据集。实验结果表明，我们的方法可以获得高分类精度和优于几种最先进的G-SSL方法。

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