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Adaptive Graph Fusion for Unsupervised Feature Selection

机译：自适应图融合用于无监督特征选择

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摘要

The massive high-dimensional data brings about great time complexity, high storage burden and poor generalization ability of learning models. Feature selection can alleviate curse of dimensionality by selecting a subset of features. Unsupervised feature selection is much challenging due to lack of label information. Most methods rely on spectral clustering to generate pseudo labels to guide feature selection in unsupervised setting. Graphs for spectral clustering can be constructed in different ways, e.g., kernel similarity, or self-representation. The construction of adjacency graphs could be affected by the parameters of kernel functions, the number of nearest neighbors or the size of the neighborhood. However, it is difficult to evaluate the effectiveness of different graphs in unsupervised feature selection. Most existing algorithms only select one graph by experience. In this paper, we propose a novel adaptive multi-graph fusion based unsupervised feature selection model (GFFS). The proposed model is free of graph selection and can combine the complementary information of different graphs. Experiments on benchmark datasets show that GFFS outperforms the state-of-the-art unsupervised feature selection algorithms.
机译：大量的高维数据带来了极大的时间复杂性，高存储负担以及学习模型的泛化能力差。通过选择特征子集，特征选择可以减轻维数的诅咒。由于缺乏标签信息，无监督的特征选择非常具有挑战性。大多数方法都依靠光谱聚类来生成伪标签，以在无人监督的情况下指导特征选择。可以以不同的方式（例如，核相似度或自表示）来构造用于频谱聚类的图。邻接图的构造可能会受到内核函数的参数，最近邻的数量或邻域大小的影响。但是，很难评估不同图形在无监督特征选择中的有效性。大多数现有算法仅凭经验选择一张图。在本文中，我们提出了一种新颖的基于自适应多图融合的无监督特征选择模型（GFFS）。所提出的模型没有图选择，并且可以结合不同图的补充信息。在基准数据集上进行的实验表明，GFFS优于最新的无监督特征选择算法。

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