首页> 外文会议>Pacific Rim International Conference on Artificial Intelligence; 20040809-20040813; Auckland; NZ >A Modified Incremental Principal Component Analysis for On-Line Learning of Feature Space and Classifier
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A Modified Incremental Principal Component Analysis for On-Line Learning of Feature Space and Classifier

机译:用于特征空间和分类器在线学习的改进增量主成分分析

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We have proposed a new concept for pattern classification systems in which feature selection and classifier learning are simultaneously carried out on-line. To realize this concept, Incremental Principal Component Analysis (IPCA) and Evolving Clustering Method (ECM) was effectively combined in the previous work. However, in order to construct a desirable feature space, a threshold value to determine the increase of a new feature shoule be properly given in the original IPCA. To alleviate this problem, we can adopt the accumulation ratio as its criterion. However, in incremental situations, the accumulation ratio must be modified every time a new sample is given. Therefore, to use this ratio as a criterion, we also need to develop a one-pass update algorithm for the ratio. In this paper, we propose an improved algorithm of IPCA in which the accumulation ratio as well as the feature space can be updated online without all the past samples. To see if correct feature construction is carried out by this new IPCA algorithm, the recognition performance is evaluated for some standard datasets when ECM is adopted as a prototype learning method in Nearest Neighbor classifier.
机译:我们提出了一种模式分类系统的新概念,其中特征选择和分类器学习是同时在线进行的。为了实现这一概念,在先前的工作中有效地结合了增量主成分分析(IPCA)和演化聚类方法(ECM)。然而,为了构造期望的特征空间,在原始IPCA中适当地给出确定新特征的增加的阈值。为了减轻这个问题,我们可以采用累积率作为判据。但是,在增量情况下,每次给出新样本时都必须修改累积比率。因此,要将这个比率用作标准,我们还需要开发一个用于比率的单次更新算法。在本文中,我们提出了一种改进的IPCA算法,其中可以在线累积更新比率和特征空间,而无需所有过去的样本。要查看此新IPCA算法是否能正确构建特征,当在最近邻分类器中将ECM用作原型学习方法时,对某些标准数据集的识别性能进行了评估。

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