Incremental Feature Extraction Based on Empirical Kernel Map
Incremental Feature Extraction Based on Empirical Kernel Map
复制标题
基于经验核图的增量特征提取
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
Joon Hyun Song
中科院分区：
文献类型：
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作者：
Byung;J. Shim;Changha Hwang;I. Kim;Joon Hyun Song
提出了一种新的增量核主元分析方法，用于数据的非线性特征提取。批量核主成分分析的问题是，当数据集很大时，计算变得令人望而却步。另一个问题是，为了用另一个数据更新特征向量，从头开始的整个分解都应该重新计算。该方法通过增量更新特征空间和使用经验核映射作为核函数，克服了上述问题。所提出的方法是更有效的内存需求比批核主成分，可以很容易地通过重新学习的数据进行改进。在我们的实验中，我们表明，所提出的方法是一个批核主成分的非线性数据集的分类问题的性能相当。
A new incremental kernel principal component analysis is proposed for the nonlinear feature extraction from the data. The problem of batch kernel principal component analysis is that the computation becomes prohibitive when the data set is large . Another problem is that, in order to update the eigenvectors with another data, the whole decomposition from scratch should be recomputed. The proposed method overcomes these problems by incrementally update eigenspace and using empirical kernel map as kernel function. The proposed method is more efficient in memory requirement than a batch kernel principal component and can be easily improved by re-learning the data. In our experiments we show that proposed method is comparable in performance to a batch kernel principal component for the classification problem on nonlinear data set.