基于特征演化流的存储适应学习
Storage Fit Learning with Feature Evolvable Streams
摘要：
近年来，特征进化学习得到了广泛的研究，当使用流进行学习时，旧的特征会消失，新的特征会出现。传统的方法通常假设在每个时间步的预测之后将显示标签。然而，在实践中，这种假设可能不成立，而在大多数时间步长中不会给出标签。一个很好的解决方案是利用流形正则化技术，利用以前的类似数据来帮助在线模型的改进。然而，这种方法需要存储所有先前的数据，这在使用大量顺序到达的流进行学习时是不可能的。因此，我们需要一个缓冲区来存储其中的一部分。考虑到不同的设备可能有不同的存储预算，学习方法应该是灵活的存储预算限制。在本文中，我们提出了一个新的设置：适应的可进化流学习（SF $^2$ EL），它将很少提供标签的问题纳入特征进化。我们的框架能够适应不同的存储预算时，学习与未标记的数据特征可进化流的行为。此外，理论和实验结果都验证了我们的方法可以保持原始特征进化学习的优点，即，可以始终跟踪最佳基线，从而在任何时间步长都能表现良好。
英文摘要：
Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usually assume that a label will be revealed after prediction at each time step. However, in practice, this assumption may not hold whereas no label will be given at most time steps. A good solution is to leverage the technique of manifold regularization to utilize the previous similar data to assist the refinement of the online model. Nevertheless, this approach needs to store all previous data which is impossible in learning with streams that arrive sequentially in large volume. Thus we need a buffer to store part of them. Considering that different devices may have different storage budgets, the learning approaches should be flexible subject to the storage budget limit. In this paper, we propose a new setting: Storage-Fit Feature-Evolvable streaming Learning (SF$^2$EL) which incorporates the issue of rarely-provided labels into feature evolution. Our framework is able to fit its behavior to different storage budgets when learning with feature evolvable streams with unlabeled data. Besides, both theoretical and empirical results validate that our approach can preserve the merit of the original feature evolvable learning i.e., can always track the best baseline and thus perform well at any time step.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
