Joint dictionary and graph learning for unsupervised feature selection
Joint dictionary and graph learning for unsupervised feature selection
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DOI：
10.1007/s10489-019-01561-x
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发表时间：
2020-01
影响因子：
5.3
通讯作者：
Deqiong Ding;Fei Xia;Xiaogao Yang;Chang Tang
中科院分区：
文献类型：
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作者：
Deqiong Ding;Fei Xia;Xiaogao Yang;Chang Tang
随着未标记和高维数据的爆炸式增长，无监督特征选择已成为机器学习中的一个关键和具有挑战性的问题。最近，基于数据表示的模型已被成功地部署用于无监督特征选择，其将特征重要性定义为通过重构函数表示原始数据的能力。然而，现有的算法大多在原始特征空间上进行特征选择，这会受到原始特征空间中噪声和冗余特征的影响。本文研究了如何在数据的字典基空间上进行特征选择，从而能够捕获比原始低层表示更高层次和更抽象的表示。此外，一个相似性图的同时学习，以保持局部的几何数据结构，已被确认为无监督的特征选择的关键。总之，我们提出了一个模型（简称为DGL-UFS）集成字典学习，相似图学习和特征选择到一个统一的框架。在不同类型的真实的世界数据集上的实验证明了所提出的框架DGL-UFS的有效性。
With the explosion of unlabelled and high-dimensional data, unsupervised feature selection has become an critical and challenging problem in machine learning. Recently, data representation based model has been successfully deployed for unsupervised feature selection, which defines feature importance as the capability to represent original data via a reconstruction function. However, most existing algorithms conduct feature selection on original feature space, which will be affected by the noisy and redundant features of original feature space. In this paper, we investigate how to conduct feature selection on the dictionary basis space of the data, which can capture higher level and more abstract representation than original low-level representation. In addition, a similarity graph is learned simultaneously to preserve the local geometrical data structure which has been confirmed critical for unsupervised feature selection. In summary, we propose a model (referred to as DGL-UFS briefly) to integrate dictionary learning, similarity graph learning and feature selection into a uniform framework. Experiments on various types of real world datasets demonstrate the effectiveness of the proposed framework DGL-UFS.