A Modified Incremental Principal Component Analysis for On-Line Learning of Feature Space and Classifier
A Modified Incremental Principal Component Analysis for On-Line Learning of Feature Space and Classifier
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DOI：
10.1007/978-3-540-28633-2_26
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发表时间：
2004-08
期刊：
影响因子：
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通讯作者：
S. Ozawa;Shaoning Pang;N. Kasabov
中科院分区：
文献类型：
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作者：
S. Ozawa;Shaoning Pang;N. Kasabov
我们提出了一个新的概念，模式分类系统中的特征选择和分类器学习同时进行在线。为了实现这一概念，增量主成分分析（IPCA）和进化聚类方法（ECM）有效地结合在以前的工作。然而，为了构造一个理想的特征空间，一个阈值，以确定一个新的特征的增加，应适当地给定在原始IPCA。为了缓解这一问题，我们可以采用积累率作为其标准。然而，在增量情况下，每次给出新样本时，必须修改累积比率。因此，为了使用该比率作为标准，我们还需要开发针对该比率的一次更新算法。本文提出了一种改进的IPCA算法，该算法不需要过去的所有样本，就可以在线更新累积比和特征空间。为了验证该算法是否能够正确构造特征，在最近邻分类器中采用ECM作为原型学习方法时，对一些标准数据集进行了识别性能评估。
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 on-line 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.