Probabilistic principal component analysis based on JoyStick Probability Selector
Probabilistic principal component analysis based on JoyStick Probability Selector
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DOI：
10.1109/ijcnn.2009.5178696
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
M. Jankovic;Masashi Sugiyama
中科院分区：
文献类型：
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作者：
M. Jankovic;Masashi Sugiyama
主成分分析（PCA）是一种常用的数据分析和处理技术，例如压缩或聚类。在本文中，我们提出了一个概率PCA模型的基础上玻恩规则。在离线实现中，它可以被看作是一个连续的优化问题。在在线实现中，将通过引入两个不同的时间尺度来解决。结果表明，最近提出的用于实现生物学上合理的PCA神经网络的面向时间的分层方法代表了所提出模型的特殊情况。该模型给出了一个通用的框架，用于创建不同的PCA实现/算法。特定的实现可以优化计算的局部性、收敛速度、精确度或一些其他感兴趣的参数。我们将提出一些实验结果来说明所提出的模型的有效性。
Principal component analysis (PCA) is a commonly applied technique for data analysis and processing, e.g. compression or clustering. In this paper we propose a probabilistic PCA model based on the Born rule. In off-line realization it can be seen as a successive optimization problem. In the on-line realization it will be solved by introduction of two different time scales. It will be shown that recently proposed time oriented hierarchical method, used for realization of biologically plausible PCA neural networks, represents a special case of the proposed model. The proposed model gives a general framework for creating different PCA realizations/algorithms. A particular realization can optimize locality of calculation, convergence speed, preciseness or some other parameter of interest. We will present some experimental results to illustrate effectiveness of the proposed model.