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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm
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Unsupervised feature selection using clustering ensembles and population based incremental learning algorithm

机译：使用聚类集成和基于总体的增量学习算法进行无监督特征选择

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This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar clustering solution to the one obtained by an ensemble learning algorithm. In particular, a clustering solution is firstly achieved by a clustering ensembles method, then the population based incremental learning algorithm is adopted to find the feature subset that best fits the obtained clustering solution. One advantage of the proposed unsupervised feature selection algorithm is that it is dimensionality-unbiased. In addition, the proposed unsupervised feature selection algorithm leverages the consensus across multiple clustering solutions. Experimental results on several real data sets demonstrate that the proposed unsupervised feature selection algorithm is often able to obtain a better feature subset when compared with other existing unsupervised feature selection algorithms. (c) 2008 Elsevier Ltd. All rights reserved.
机译：本文介绍了一种新的无监督聚类特征选择算法，该算法结合了聚类集成方法和基于种群的增量学习算法。提出的无监督特征选择算法的主要思想是搜索所有特征的子集，以使在该特征子集上训练的聚类算法可以实现与通过集成学习算法获得的聚类解决方案最相似的聚类解决方案。特别地，首先通过聚类集成方法获得聚类解决方案，然后采用基于总体的增量学习算法来找到最适合所获得的聚类解决方案的特征子集。提出的无监督特征选择算法的一个优点是它是无维数的。另外，提出的无监督特征选择算法利用了跨多个聚类解决方案的共识。在几个真实数据集上的实验结果表明，与其他现有的非监督特征选择算法相比，所提出的非监督特征选择算法通常能够获得更好的特征子集。 （c）2008 Elsevier Ltd.保留所有权利。

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