A Multipurpose Linear Component Analysis Method Based on Modulated Hebb-Oja Learning Rule
A Multipurpose Linear Component Analysis Method Based on Modulated Hebb-Oja Learning Rule
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DOI：
10.1109/lsp.2008.2002710
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发表时间：
2008-11
影响因子：
3.9
通讯作者：
M. Jankovic;Masashi Sugiyama
中科院分区：
文献类型：
--
作者：
M. Jankovic;Masashi Sugiyama
这封信提出了一个Hebb型学习算法的在线线性计算的主成分。所提出的方法是基于最近提出的合作竞争的概念，命名为面向时间的分层方法。该算法对信号功率而不是对信号本身执行紧缩。它也将显示何时，或如何，这种算法可以用作盲信号分离算法。提出的突触效能学习规则不需要其他效能值的显式信息来进行个体效能修改。所需的全局计算电路的数量是一个。
This letter presents a Hebb-type learning algorithm for online linear calculation of principal components. The proposed method is based on a recently proposed cooperative-competitive concept, named the time-oriented hierarchical method. The algorithm performs deflation on the signal power rather than on the signal itself. It will be also shown when, or how, this algorithm can be used as a blind signal separation algorithm. The proposed synaptic efficacy learning rule does not need the explicit information about the value of the other efficacies to make individual efficacy modification. The number of necessary global calculation circuits is one.