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Comparison between Supervised and Unsupervised Feature Selection Methods

机译：监督和无监督特征选择方法的比较

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The reduction of the feature set by selecting relevant features for the classification process is an important step within the image processing chain, but sometimes too little attention is paid to it. Such a reduction has many advantages. It can remove irrelevant and redundant data, improve recognition performance, reduce storage capacity requirements, computational time of calculations and also the complexity of the model. Within this paper supervised and unsupervised feature selection methods are compared with respect to the achievable recognition accuracy. Supervised Methods include information of the given classes in the selection, whereas unsupervised ones can be used for tasks without known class labels. Feature clustering is an unsupervised method. For this type of feature reduction, mainly hierarchical methods, but also k-means are used. Instead of this two clustering methods, the Expectation Maximization (EM) algorithm was used in this paper. The aim is to investigate whether this type of clustering algorithm can provide a proper feature vector using feature clustering. There is no feature reduction technique that provides equally best results for all datasets and classifiers. However, for all datasets, it was possible to reduce the feature set to a specific number of useful features without losses and often even with improvements in recognition performance.
机译：通过为分类过程选择相关特征来减少特征集是图像处理链内的重要步骤，但有时会对它付出太少的关注。这种减少有很多优点。它可以删除无关紧要和冗余数据，提高识别性能，降低存储容量要求，计算的计算时间以及模型的复杂性。在本文中，监督和无监督的特征选择方法与可实现的识别准确性进行比较。监督方法包括选择中给定类的信息，而无监督的信息可用于无需已知类标签的任务。特征群集是一种无人监督的方法。对于这种类型的特征减少，主要是分层方法，但也使用K-means。在本文中使用了预期最大化（EM）算法而不是这两个聚类方法。目的是研究这种类型的聚类算法是否可以使用特征群集提供适当的特征向量。没有特征减少技术，为所有数据集和分类器提供了同样最佳效果。但是，对于所有数据集，可以将特征减少到特定数量的有用功能，而不会损失，并且通常是识别性能的改进。

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