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Feature Subset Selection and Ranking for Data Dimensionality Reduction

机译:特征子集选择和排名以减少数据维数

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A new unsupervised forward orthogonal search (FOS) algorithm is introduced for feature selection and ranking. In the new algorithm, features are selected in a stepwise way, one at a time, by estimating the capability of each specified candidate feature subset to represent the overall features in the measurement space. A squared correlation function is employed as the criterion to measure the dependency between features and this makes the new algorithm easy to implement. The forward orthogonalization strategy, which combines good effectiveness with high efficiency, enables the new algorithm to produce efficient feature subsets with a clear physical interpretation
机译:引入了一种新的无监督前向正交搜索(FOS)算法进行特征选择和排序。在新算法中,通过估计每个指定的候选特征子集代表测量空间中总体特征的能力,以逐步的方式一次选择一个特征。采用平方相关函数作为度量特征之间相关性的标准,这使得新算法易于实现。前向正交化策略结合了良好的有效性和高效率,使新算法能够生成具有清晰物理解释的有效特征子集

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