摘要翻译：
慢速特征分析(SFA)在一个时间相干的高维原始感觉输入信号中提取代表变化的潜在原因的特征。我们新的增量式SFA（IncSFA）结合了增量式主成分分析和次成分分析。与基于批处理的标准SFA不同，IncSFA适应于非平稳环境，易于训练，不受异常值的破坏，且无协方差。这些特性使IncSFA成为一个普遍适用于自主学习代理和机器人的无监督预处理器。在IncSFA中，CCIPCA和MCA的更新采用了Hebbian和反Hebbian更新的形式，扩展了SFA的生物学合理性。在单节点和深度网络版本中，IncSFA学习通过表示有意义的抽象环境属性的信息缓慢特征来编码其输入流（如高维视频）。它可以处理批量SFA失败的情况。
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英文标题：
《Incremental Slow Feature Analysis: Adaptive and Episodic Learning from
  High-Dimensional Input Streams》
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作者：
Varun Raj Kompella, Matthew Luciw and Juergen Schmidhuber
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Slow Feature Analysis (SFA) extracts features representing the underlying causes of changes within a temporally coherent high-dimensional raw sensory input signal. Our novel incremental version of SFA (IncSFA) combines incremental Principal Components Analysis and Minor Components Analysis. Unlike standard batch-based SFA, IncSFA adapts along with non-stationary environments, is amenable to episodic training, is not corrupted by outliers, and is covariance-free. These properties make IncSFA a generally useful unsupervised preprocessor for autonomous learning agents and robots. In IncSFA, the CCIPCA and MCA updates take the form of Hebbian and anti-Hebbian updating, extending the biological plausibility of SFA. In both single node and deep network versions, IncSFA learns to encode its input streams (such as high-dimensional video) by informative slow features representing meaningful abstract environmental properties. It can handle cases where batch SFA fails.
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PDF链接：
https://arxiv.org/pdf/1112.2113