Abstract The studies of hidden complex structures in data have popularized the use of graph-based learning methods in semi-supervised and unsupervised learning tasks. Kernelized graph-based methods are proven to perform better, but these methods suffer from the issue of appropriate kernel selection. Instead of using multiple views, these methods generally use a single view. But multi-view methods need a proper weight assignment technique to each view in proportion to their contribution to the learning task. To solve this, a novel Self-weighted Multi-view Multiple Kernel Learning (SMVMKL) framework is proposed using multiple kernels on multiple views that automatically assigns appropriate weight to each kernel of each view without introducing an additional parameter. But the real-world data that is either noisy or corrupt with outliers which may effect the performance of the proposed SMVMKL method. To deal with this, a Robust Self-weighted Multi-view Multiple Kernel Learning (RSMVMKL) framework using the l2,1-norm has also been proposed that reduces the effect of outliers present in the data set. Both the proposed methods have been evaluated on multiple benchmark data sets and result in a performance comparable with the other state-of-the-art multi-view methods considered in this paper.

中文翻译：

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健壮的内核化基于图的学​​习

摘要 数据中隐藏复杂结构的研究已经普及了基于图的学​​习方法在半监督和无监督学习任务中的使用。事实证明，基于核化图的方法性能更好，但这些方法存在适当核选择的问题。这些方法通常使用单个视图，而不是使用多个视图。但是多视图方法需要根据每个视图对学习任务的贡献对每个视图进行适当的权重分配技术。为了解决这个问题，提出了一种新颖的自加权多视图多核学习 (SMVMKL) 框架，该框架在多个视图上使用多个内核，自动为每个视图的每个内核分配适当的权重，而无需引入额外的参数。但是现实世界的数据要么是嘈杂的要么是带有异常值的损坏，这可能会影响所提出的 SMVMKL 方法的性能。为了解决这个问题，还提出了使用 l2,1-norm 的鲁棒自加权多视图多核学习 (RSMVMKL) 框架，以减少数据集中存在的异常值的影响。所提出的两种方法都已在多个基准数据集上进行了评估，其性能可与本文中考虑的其他最先进的多视图方法相媲美。