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, ℓ21-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.

中文翻译：

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多视图投影聚类与图学习。

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。但是，大多数现有方法都是直接从原始的高维数据构造图，而这些数据在实际应用中始终包含冗余，噪声和异常项，从而导致图的可靠性和准确性不高。此外，他们没有有效地选择一些有用的功能，这些功能对于图学习和聚类很重要。为了解决这些限制，我们提出了一个新颖的模型，该模型将降维，流形结构学习和特征选择组合到一个框架中。我们将高维数据映射到低维空间中，以减少算法的复杂性并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形。更进一步，采用ℓ21范数正则化来自适应地选择一些有助于改善聚类性能的重要特征。最后，提出了一种有效的算法来求解最优解。在一些基准数据集上的大量实验结果证明了该方法的优越性。