关于论文中related work结构书写记录 |
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1.类型：分模块介绍
论文：《Discovering Multiple Co-Clusterings With Matrix Factorization》 写法： This article is closely related to two branches of study, co-clustering and multiple clusterings. In the following two sections, we give a brief review of the work that lead to our own. section A... section B... To combat with this challengeing task, we introduce the MultiCC, which will be presented in the following section. MultiCC can find out multiple diverse co-clusterings with good quality, as we will show in the experiments.关注自己的工作的模型架构，分section介绍基础模型，以及对模型的变种（这一部分要添加相关工作之间的联系，及存在的问题改进，加强前后工作之间的联系），并说明自己工作对此的改进，引出对于自己的模型。 这种related work是模型结构性较强的一种，所以解释性较强。 一般由自己模型决定related work的内容分布。 2.类型：领域内方法总结介绍论文：《Self-Supervised Deep Multi-View Subspace Clustering》 写法： Multi-view clustering is an important and hot research topic in multi-view data analysis. To date, various methods have been proposed by assuming the data points are drawn from multiple low-dimensional subspaces. Based on different techniques, existing subspace methods can be roughly divided into three groups.首先根据自己所研究的领域方法，如“Multi-View Subspace Clustering”，进行不同类型方法的总结。写法最好是循序渐进，可以描述出不同类型方法之间存在的问题联系，使之不孤立。例如文中提到，基于CCA的多视图聚类存在了什么问题，因此去寻求解决方法所以出现了基于NMF的方法，其中有哪些优点。。而相对于另外一种方法，则可以以另外的方式进行描述。 如果related work分为两部分，则需要有过渡句（是真的可以呈上启下的过渡，而不是为了过渡而过渡）。通过过渡句，开启对自己模型的一个概括。 写法： Even though the aforementioned methods obtain promising results, they didn’t take the noise ubiquitous in real-world datasets into account. The noise and outlying entries in the real-world datasets will impair the clustering performance. #下面开始引入自己方法描述 Thus, in this paper, we aimto simultaneously capture complex structures and reduce the effect of noise on clustering performance by introducing supervised information.写出前序模型未解决的问题，引出自己模型的切入点。 未完。。 |
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