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Robust Joint Feature Weights Learning Framework

机译：稳健的联合特征权重学习框架

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摘要

Feature selection, selecting the most informative subset of features, is an important research direction in dimension reduction. The combinatorial search in feature selection is essentially a binary optimization problem, known as NP hard, which can be alleviated by learning feature weights. Traditional feature weights algorithms rely on heuristic search path. These approaches neglect the interaction and dependency between different features, and thus provide no guarantee for optimality. In this paper, we propose a novel joint feature weights learning framework, which imposes both nonnegative and -norm constraints on the feature weights matrix. The nonnegative property ensures the physical significance of learned feature weights. Meanwhile, -norm minimization achieves joint selection of the most relevant features by exploiting the whole feature space. More importantly, an efficient iterative algorithm with proved convergence is designed to optimize a convex objective function. Using this framework as a platform, we propose new supervised and unsupervised joint feature selection methods. Particularly, in the proposed unsupervised method, nonnegative graph embedding is developed to exploit intrinsic structure in the weighted space. Comparative experiments on seven real-world data sets indicate that our framework is both effective and efficient.
机译：选择特征最多的特征子集的特征选择是降维的重要研究方向。特征选择中的组合搜索本质上是一个二元优化问题，称为NP hard，可以通过学习特征权重来缓解。传统的特征权重算法依赖于启发式搜索路径。这些方法忽略了不同特征之间的相互作用和依赖性，因此无法保证最优性。在本文中，我们提出了一种新颖的联合特征权重学习框架，该框架在特征权重矩阵上施加了非负约束和-norm约束。非负属性确保学习的特征权重的物理重要性。同时，-norm最小化通过利用整个特征空间实现对最相关特征的联合选择。更重要的是，设计了一种有效的迭代算法并证明了收敛性，以优化凸目标函数。以此框架为平台，我们提出了新的有监督和无监督联合特征选择方法。特别是，在提出的无监督方法中，非负图嵌入被开发来利用加权空间中的固有结构。在七个真实世界的数据集上进行的比较实验表明，我们的框架既有效又高效。

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