Multi-view projected clustering with graph learning
Multi-view projected clustering with graph learning
复制标题
具有图学习的多视图投影聚类
DOI：
10.1016/j.neunet.2020.03.020
复制
发表时间：
2020
期刊：
影响因子：
7.8
通讯作者：
Ling Shao
中科院分区：
文献类型：
--
作者：
Quanxue Gao;Zhizhen Wan;Ying Liang;Qianqian Wang;Yang Liu;Ling Shao
基于图的多视图学习由于其有效性和良好的聚类性能而众所周知。然而，在真实的应用中，大多数方法都是直接从原始高维数据中构造图，这些数据往往含有冗余、噪声和离群项，从而导致图的不可靠和不准确。此外，它们不能有效地选择一些有用的特征，这些特征对图学习和聚类很重要。为了解决这些限制，我们提出了一种新的模型，结合降维，流形结构学习和特征选择到一个框架。我们将高维数据映射到低维空间，以降低算法的复杂度，减少噪声和冗余的影响。因此，我们可以自适应地学习更准确的图。此外，还采用21范数正则化方法自适应地选择一些重要的特征，以提高聚类性能。最后，提出了一种求解最优解的有效算法。在一些基准数据集上的实验结果证明了该方法的优越性。
Graph based multi-view learning is well known due to its effectiveness and good clustering performance. However, most existing methods directly construct graph from original high-dimensional data which always contain redundancy, noise and outlying entries in real applications, resulting in unreliable and inaccurate graph. Moreover, they do not effectively select some useful features which are important for graph learning and clustering. To solve these limits, we propose a novel model that combines dimensionality reduction, manifold structure learning and feature selection into a framework. We map high-dimensional data into low-dimensional space to reduce the complexity of the algorithm and reduce the effect of noise and redundance. Therefore, we can adaptively learn a more accurate graph. Further more, ℓ 21-norm regularization is adopted to adaptively select some important features which help improve clustering performance. Finally, an efficiently algorithm is proposed to solve the optimal solution. Extensive experimental results on some benchmark datasets demonstrate the superiority of the proposed method.