数据的超类表示
Hyper-Class Representation of Data
摘要：
数据表示通常是一个自然的形式与他们的属性值。在此基础上，数据处理是以属性为中心的计算。然而，以属性为中心的计算存在三个局限性，即计算不灵活、偏好计算和输出不令人满意。为了解决这些问题，提出了一种新的数据表示方法--超类表示法。首先定义了数据特征的交叉熵、KL散度和JS散度。利用这三个参数可以发现数据中的超类。最后，通过一种推荐算法对所提出的数据超类表示进行了评价，结果表明，超类表示能够为推荐系统提供真正有用的参考信息，并且推荐效果远优于现有的推荐算法，这种方法是有效和有前途的。
英文摘要：
Data representation is usually a natural form with their attribute values. On this basis, data processing is an attribute-centered calculation. However, there are three limitations in the attribute-centered calculation, saying, inflexible calculation, preference computation, and unsatisfactory output. To attempt the issues, a new data representation, named as hyper-classes representation, is proposed for improving recommendation. First, the cross entropy, KL divergence and JS divergence of features in data are defined. And then, the hyper-classes in data can be discovered with these three parameters. Finally, a kind of recommendation algorithm is used to evaluate the proposed hyper-class representation of data, and shows that the hyper-class representation is able to provide truly useful reference information for recommendation systems and makes recommendations much better than existing algorithms, i.e., this approach is efficient and promising.
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。
