机器学习数据的压缩表示
Condensed Representation of Machine Learning Data
摘要：
机器学习模型的训练需要足够的数据。数据的充分性并不总是与数量有关，而是与相关性和减少冗余有关。数据生成过程会产生大量数据。当使用原始数据时，这样的大数据会导致大量的计算资源使用。代替使用原始数据，可以使用适当的压缩表示。结合K-means，一个著名的聚类方法，一些校正和细化设施，一个新的凝聚表示方法的机器学习的应用。为了有意义地和直观地呈现这种新方法，采用了综合生成的数据。已经表明，通过使用压缩表示而不是原始数据，可以进行可接受的准确模型训练。
英文摘要：
Training of a Machine Learning model requires sufficient data. The sufficiency of the data is not always about the quantity, but about the relevancy and reduced redundancy. Data-generating processes create massive amounts of data. When used raw, such big data is causing much computational resource utilization. Instead of using the raw data, a proper Condensed Representation can be used instead. Combining K-means, a well-known clustering method, with some correction and refinement facilities a novel Condensed Representation method for Machine Learning applications is introduced. To present the novel method meaningfully and visually, synthetically generated data is employed. It has been shown that by using the condensed representation, instead of the raw data, acceptably accurate model training is possible.
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。
