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Incremental learning of feature space and classifier for on-line pattern recognition

机译:特征空间和分类器的增量学习,用于在线模式识别

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摘要

In the previous work, we have proposed a new approach to pattern recognition tasks, in which not only a classifier but also a feature space is trained incrementally. To implement this idea, Incremental Principal Component Analysis (IPCA) and Evolving Clustering Method (ECM) were effectively combined. However, the original IPCA only gives a way to determine the increase of a new feature based on a threshold value, whose value must be optimized for different datasets. In this paper, to alleviate the dependency on datasets, the accumulation ratio is introduced as its criterion, and an improved algorithm of IPCA is derived. To see if correct feature construction is carried out by this new IPCA algorithm, the classification performance is evaluated over some standard datasets when Evolving Clustering Method (ECM) is adopted as a prototype learning method for Nearest Neighbor classifier. Our simulation results show that the proposed IPCA works well without elaborating sensitive parameter optimization and its recognition accuracy outperforms that of the previous model.
机译:在之前的工作中,我们提出了一种新的模式识别任务方法,其中不仅对分类器而且对特征空间进行增量训练。为了实现这一想法,有效地结合了增量主成分分析(IPCA)和演化聚类方法(ECM)。但是,原始IPCA仅提供了一种基于阈值确定新功能增加的方法,该阈值必须针对不同的数据集进行优化。为了减轻对数据集的依赖,以累积率为准则,推导了改进的IPCA算法。要查看此新IPCA算法是否正确构建了特征,当采用进化聚类方法(ECM)作为最近邻分类器的原型学习方法时,对某些标准数据集的分类性能进行了评估。我们的仿真结果表明,所提出的IPCA在不进行敏感参数优化的情况下仍能很好地工作,并且其识别精度优于先前模型。

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