An online incremental orthogonal component analysis method for dimensionality reduction
An online incremental orthogonal component analysis method for dimensionality reduction
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DOI：
10.1016/j.neunet.2016.10.001
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Tao Zhu;Ye Xu;S. Furao;Jinxi Zhao
中科院分区：
文献类型：
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作者：
Tao Zhu;Ye Xu;S. Furao;Jinxi Zhao
本文介绍了一种快速的线性降维方法增量正交分量分析（IOCA）。IOCA被设计为在在线环境中自动提取所需的正交分量（OC）。原始数据的OC和低维表示仅通过整个数据集一次获得。IOCA算法无需求解矩阵特征值问题或矩阵求逆问题，以较低的计算代价从连续数据流中进行增量学习。IOCA通过提出自适应阈值策略，能够自动确定特征子空间的维数。同时，保证了学习后的OC的质量。分析和实验表明，IOCA算法简单，但效率和效果。
In this paper, we introduce a fast linear dimensionality reduction method named incremental orthogonal component analysis (IOCA). IOCA is designed to automatically extract desired orthogonal components (OCs) in an online environment. The OCs and the low-dimensional representations of original data are obtained with only one pass through the entire dataset. Without solving matrix eigenproblem or matrix inversion problem, IOCA learns incrementally from continuous data stream with low computational cost. By proposing an adaptive threshold policy, IOCA is able to automatically determine the dimension of feature subspace. Meanwhile, the quality of the learned OCs is guaranteed. The analysis and experiments demonstrate that IOCA is simple, but efficient and effective.